{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T05:18:54Z","timestamp":1773119934734,"version":"3.50.1"},"reference-count":204,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2022,9,17]],"date-time":"2022-09-17T00:00:00Z","timestamp":1663372800000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021YFC2500203"],"award-info":[{"award-number":["2021YFC2500203"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDA16021400"],"award-info":[{"award-number":["XDA16021400"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["LY20C060001"],"award-info":[{"award-number":["LY20C060001"]}]},{"name":"Innovation Fund of Institute of Computing and Technology","award":["E161080"],"award-info":[{"award-number":["E161080"]}]},{"name":"Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology","award":["JBZX-202003"],"award-info":[{"award-number":["JBZX-202003"]}]},{"name":"Shandong First Medical University & Shandong Academy of Medical Sciences","award":["922-001003130 185RC"],"award-info":[{"award-number":["922-001003130 185RC"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,11,19]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>In common medical procedures, the time-consuming and expensive nature of obtaining test results plagues doctors and patients. Digital pathology research allows using computational technologies to manage data, presenting an opportunity to improve the efficiency of diagnosis and treatment. Artificial intelligence (AI) has a great advantage in the data analytics phase. Extensive research has shown that AI algorithms can produce more up-to-date and standardized conclusions for whole slide images. In conjunction with the development of high-throughput sequencing technologies, algorithms can integrate and analyze data from multiple modalities to explore the correspondence between morphological features and gene expression. This review investigates using the most popular image data, hematoxylin\u2013eosin stained tissue slide images, to find a strategic solution for the imbalance of healthcare resources. The article focuses on the role that the development of deep learning technology has in assisting doctors\u2019 work and discusses the opportunities and challenges of AI.<\/jats:p>","DOI":"10.1093\/bib\/bbac367","type":"journal-article","created":{"date-parts":[[2022,9,20]],"date-time":"2022-09-20T08:28:20Z","timestamp":1663662500000},"source":"Crossref","is-referenced-by-count":31,"title":["Multi-modality artificial intelligence in digital pathology"],"prefix":"10.1093","volume":"23","author":[{"given":"Yixuan","family":"Qiao","sequence":"first","affiliation":[{"name":"Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences , Beijing 100190, China"},{"name":"University of Chinese Academy of Sciences , Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7736-2443","authenticated-orcid":false,"given":"Lianhe","family":"Zhao","sequence":"additional","affiliation":[{"name":"Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences , Beijing 100190, China"}]},{"given":"Chunlong","family":"Luo","sequence":"additional","affiliation":[{"name":"Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences , Beijing 100190, China"},{"name":"University of Chinese Academy of Sciences , Beijing 100049, China"}]},{"given":"Yufan","family":"Luo","sequence":"additional","affiliation":[{"name":"Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences , Beijing 100190, China"},{"name":"University of Chinese Academy of Sciences , Beijing 100049, China"}]},{"given":"Yang","family":"Wu","sequence":"additional","affiliation":[{"name":"Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences , Beijing 100190, China"}]},{"given":"Shengtong","family":"Li","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology , Cambridge, MA 02139, USA"}]},{"given":"Dechao","family":"Bu","sequence":"additional","affiliation":[{"name":"Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences , Beijing 100190, China"}]},{"given":"Yi","family":"Zhao","sequence":"additional","affiliation":[{"name":"Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences , Beijing 100190, China"},{"name":"University of Chinese Academy of Sciences , Beijing 100049, China"},{"name":"Shandong First Medical University & Shandong Academy of Medical Sciences , Shandong Ji\u2019nan 250117, China"}]}],"member":"286","published-online":{"date-parts":[[2022,9,16]]},"reference":[{"issue":"4","key":"2022112110592691000_ref1","doi-asserted-by":"crossref","first-page":"1700391","DOI":"10.1183\/13993003.00391-2017","article-title":"What is precision medicine?","volume":"50","author":"Konig","year":"2017","journal-title":"Eur Respir J"},{"issue":"4","key":"2022112110592691000_ref2","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1038\/s41416-020-01122-x","article-title":"Deep learning in cancer pathology: a new generation of clinical biomarkers","volume":"124","author":"Echle","year":"2021","journal-title":"Br J Cancer"},{"issue":"6","key":"2022112110592691000_ref3","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1101\/pdb.prot073411","article-title":"Manual hematoxylin and eosin staining of mouse tissue sections","volume":"2014","author":"Cardiff","year":"2014","journal-title":"Cold Spring Harb Protoc"},{"key":"2022112110592691000_ref4","first-page":"pdb.prot4986","article-title":"Hematoxylin and eosin staining of tissue and cell sections","volume":"2008","author":"Fischer","year":"2008","journal-title":"CSH Protoc"},{"key":"2022112110592691000_ref5","doi-asserted-by":"crossref","first-page":"40","DOI":"10.4103\/jpi.jpi_69_18","article-title":"Twenty years of digital pathology: an overview of the road travelled, what is on the horizon, and the emergence of vendor-neutral archives","volume":"9","author":"Pantanowitz","year":"2018","journal-title":"J Pathol Inform"},{"issue":"7553","key":"2022112110592691000_ref6","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"2022112110592691000_ref7","first-page":"5709","volume-title":"2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (Cvpr)","author":"Chang"},{"key":"2022112110592691000_ref8","doi-asserted-by":"crossref","first-page":"58443","DOI":"10.1109\/ACCESS.2020.2983149","article-title":"A survey of autonomous driving: common practices and emerging technologies","volume":"8","author":"Yurtsever","year":"2020","journal-title":"IEEE Access"},{"key":"2022112110592691000_ref9","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med Image Anal"},{"issue":"12","key":"2022112110592691000_ref10","doi-asserted-by":"crossref","first-page":"2065","DOI":"10.1158\/2326-6066.CIR-19-0311","article-title":"Tumor immune microenvironment and chemosensitivity signature for predicting response to chemotherapy in gastric cancer","volume":"7","author":"Jiang","year":"2019","journal-title":"Cancer Immunol Res"},{"key":"2022112110592691000_ref11","first-page":"1","volume-title":"2017 15th International Conference on ICT and Knowledge Engineering (ICT&KE)","author":"Ongsulee","year":"2017"},{"key":"2022112110592691000_ref12","first-page":"738","volume-title":"Pattern Recognition and Machine Learning","author":"Bishop","year":"2006"},{"issue":"2","key":"2022112110592691000_ref13","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1023\/A:1007465528199","article-title":"Bayesian network classifiers","volume":"29","author":"Friedman","year":"1997","journal-title":"Mach Learn"},{"issue":"1","key":"2022112110592691000_ref14","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/BF00116251","article-title":"Induction of decision trees","volume":"1","author":"Quinlan","year":"1986","journal-title":"Mach Learn"},{"issue":"3","key":"2022112110592691000_ref15","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach Learn"},{"issue":"7","key":"2022112110592691000_ref16","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1038\/s41576-019-0122-6","article-title":"Deep learning: new computational modelling techniques for genomics","volume":"20","author":"Eraslan","year":"2019","journal-title":"Nat Rev Genet"},{"issue":"1","key":"2022112110592691000_ref17","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1186\/s13073-019-0689-8","article-title":"Artificial intelligence in clinical and genomic diagnostics","volume":"11","author":"Dias","year":"2019","journal-title":"Genome Med"},{"issue":"1","key":"2022112110592691000_ref18","doi-asserted-by":"crossref","first-page":"5057","DOI":"10.1038\/s41467-020-18677-1","article-title":"A deep learning approach to programmable RNA switches","volume":"11","author":"Angenent-Mari","year":"2020","journal-title":"Nat Commun"},{"issue":"11","key":"2022112110592691000_ref19","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc IEEE"},{"key":"2022112110592691000_ref20","doi-asserted-by":"crossref","first-page":"770","DOI":"10.1109\/CVPR.2016.90","volume-title":"2016 IEEE Conference on Computer Vision and Pattern Recognition (Cvpr)","author":"He","year":"2016"},{"issue":"8","key":"2022112110592691000_ref21","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1038\/s41568-018-0016-5","article-title":"Artificial intelligence in radiology","volume":"18","author":"Hosny","year":"2018","journal-title":"Nat Rev Cancer"},{"issue":"2","key":"2022112110592691000_ref22","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.zemedi.2018.11.002","article-title":"An overview of deep learning in medical imaging focusing on MRI","volume":"29","author":"Lundervold","year":"2019","journal-title":"Z Med Phys"},{"issue":"1","key":"2022112110592691000_ref23","doi-asserted-by":"crossref","first-page":"2449","DOI":"10.1038\/s41467-019-10168-2","article-title":"Detection of DNA base modifications by deep recurrent neural network on Oxford Nanopore sequencing data","volume":"10","author":"Liu","year":"2019","journal-title":"Nat Commun"},{"issue":"1","key":"2022112110592691000_ref24","doi-asserted-by":"crossref","first-page":"5407","DOI":"10.1038\/s41467-019-13395-9","article-title":"RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning","volume":"10","author":"Singh","year":"2019","journal-title":"Nat Commun"},{"issue":"5","key":"2022112110592691000_ref25","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1038\/s42256-019-0052-1","article-title":"Pathologist-level interpretable whole-slide cancer diagnosis with deep learning","volume":"1","author":"Zhang","year":"2019","journal-title":"Nat Mach Intell"},{"key":"2022112110592691000_ref26","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1093\/bioinformatics\/btab670","article-title":"Explainable nucleus classification using decision tree approximation of learned embeddings","volume":"38","author":"Amgad","year":"2022","journal-title":"Bioinformatics"},{"issue":"3","key":"2022112110592691000_ref27","doi-asserted-by":"crossref","first-page":"e211740","DOI":"10.1001\/jamanetworkopen.2021.1740","article-title":"Point-of-care digital cytology with artificial intelligence for cervical cancer screening in a resource-limited setting","volume":"4","author":"Holmstrom","year":"2021","journal-title":"JAMA Netw Open"},{"key":"2022112110592691000_ref28","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.ejca.2021.10.007","article-title":"Integration of deep learning-based image analysis and genomic data in cancer pathology: a systematic review","volume":"160","author":"Schneider","year":"2022","journal-title":"Eur J Cancer"},{"key":"2022112110592691000_ref29","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.ejca.2021.07.012","article-title":"Gastrointestinal cancer classification and prognostication from histology using deep learning: systematic review","volume":"155","author":"Kuntz","year":"2021","journal-title":"Eur J Cancer"},{"issue":"6","key":"2022112110592691000_ref30","doi-asserted-by":"crossref","first-page":"1183","DOI":"10.1136\/gutjnl-2020-322880","article-title":"Artificial intelligence-based pathology for gastrointestinal and hepatobiliary cancers","volume":"70","author":"Calderaro","year":"2021","journal-title":"Gut"},{"key":"2022112110592691000_ref31","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1038\/s41523-018-0079-1","article-title":"Image analysis with deep learning to predict breast cancer grade, ER status, histologic subtype, and intrinsic subtype","volume":"4","author":"Couture","year":"2018","journal-title":"NPJ Breast Cancer"},{"key":"2022112110592691000_ref32","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.ymeth.2020.05.015","article-title":"A deep metric learning approach for histopathological image retrieval","volume":"179","author":"Yang","year":"2020","journal-title":"Methods"},{"key":"2022112110592691000_ref33","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.patcog.2018.03.015","article-title":"Pairwise based deep ranking hashing for histopathology image classification and retrieval","volume":"81","author":"Shi","year":"2018","journal-title":"Patt Recogn"},{"issue":"8","key":"2022112110592691000_ref34","doi-asserted-by":"crossref","first-page":"1962","DOI":"10.1109\/TMI.2016.2529665","article-title":"Structure-preserving color normalization and sparse stain separation for histological images","volume":"35","author":"Vahadane","year":"2016","journal-title":"IEEE Trans Med Imaging"},{"key":"2022112110592691000_ref35","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.neucom.2021.07.005","article-title":"Towards histopathological stain invariance by unsupervised domain augmentation using generative adversarial networks","volume":"460","author":"Vasiljevi\u0107","year":"2021","journal-title":"Neurocomputing"},{"issue":"2","key":"2022112110592691000_ref36","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1109\/TPAMI.2019.2936841","article-title":"Neural image compression for gigapixel histopathology image analysis","volume":"43","author":"Tellez","year":"2019","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2022112110592691000_ref37","first-page":"47","volume-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","author":"Yang","year":"2021"},{"issue":"5","key":"2022112110592691000_ref38","doi-asserted-by":"crossref","first-page":"1196","DOI":"10.1109\/TMI.2016.2525803","article-title":"Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images","volume":"35","author":"Sirinukunwattana","year":"2016","journal-title":"IEEE Trans Med Imaging"},{"key":"2022112110592691000_ref39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.media.2019.03.014","article-title":"Fast and accurate tumor segmentation of histology images using persistent homology and deep convolutional features","volume":"55","author":"Qaiser","year":"2019","journal-title":"Med Image Anal"},{"key":"2022112110592691000_ref40","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.media.2017.07.003","article-title":"Efficient and robust cell detection: a structured regression approach","volume":"44","author":"Xie","year":"2018","journal-title":"Med Image Anal"},{"issue":"7","key":"2022112110592691000_ref41","doi-asserted-by":"crossref","first-page":"1550","DOI":"10.1109\/TMI.2017.2677499","article-title":"A dataset and a technique for generalized nuclear segmentation for computational pathology","volume":"36","author":"Kumar","year":"2017","journal-title":"IEEE Trans Med Imaging"},{"issue":"12","key":"2022112110592691000_ref42","doi-asserted-by":"crossref","first-page":"2718","DOI":"10.1109\/TMI.2018.2851150","article-title":"Deep learning global glomerulosclerosis in transplant kidney frozen sections","volume":"37","author":"Marsh","year":"2018","journal-title":"IEEE Trans Med Imaging"},{"issue":"8","key":"2022112110592691000_ref43","doi-asserted-by":"crossref","first-page":"1487","DOI":"10.1038\/s41379-021-00807-9","article-title":"Multi-magnification-based machine learning as an ancillary tool for the pathologic assessment of shaved margins for breast carcinoma lumpectomy specimens","volume":"34","author":"D'Alfonso","year":"2021","journal-title":"Mod Pathol"},{"issue":"1","key":"2022112110592691000_ref44","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1681\/ASN.2020050597","article-title":"Deep learning-based segmentation and quantification in experimental kidney histopathology","volume":"32","author":"Bouteldja","year":"2021","journal-title":"J Am Soc Nephrol"},{"key":"2022112110592691000_ref45","article-title":"BrcaSeg: a deep learning approach for tissue quantification and genomic correlations of histopathological images","volume":"19","author":"Lu","year":"2021","journal-title":"Genom Proteom Bioinformatics"},{"key":"2022112110592691000_ref46","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.media.2018.12.001","article-title":"MILD-net: minimal information loss dilated network for gland instance segmentation in colon histology images","volume":"52","author":"Graham","year":"2019","journal-title":"Med Image Anal"},{"issue":"15","key":"2022112110592691000_ref47","doi-asserted-by":"crossref","DOI":"10.3390\/cancers13153811","article-title":"Deep learning for automatic subclassification of gastric carcinoma using whole-slide histopathology images","volume":"13","author":"Jang","year":"2021","journal-title":"Cancers"},{"issue":"2","key":"2022112110592691000_ref48","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1109\/TMI.2018.2865709","article-title":"Segmentation of nuclei in histopathology images by deep regression of the distance map","volume":"38","author":"Naylor","year":"2019","journal-title":"IEEE Trans Med Imaging"},{"key":"2022112110592691000_ref49","doi-asserted-by":"crossref","first-page":"586292","DOI":"10.3389\/fonc.2020.586292","article-title":"SuperHistopath: a deep learning pipeline for mapping tumor heterogeneity on low-resolution whole-slide digital histopathology images","volume":"10","author":"Zormpas-Petridis","year":"2021","journal-title":"Front Oncol"},{"key":"2022112110592691000_ref50","doi-asserted-by":"crossref","first-page":"102183","DOI":"10.1016\/j.media.2021.102183","article-title":"Joint fully convolutional and graph convolutional networks for weakly-supervised segmentation of pathology images","volume":"73","author":"Zhang","year":"2021","journal-title":"Med Image Anal"},{"issue":"4","key":"2022112110592691000_ref51","doi-asserted-by":"crossref","first-page":"1171","DOI":"10.1158\/0008-5472.CAN-20-0668","article-title":"Interactive classification of whole-slide imaging data for cancer researchers","volume":"81","author":"Lee","year":"2021","journal-title":"Cancer Res"},{"key":"2022112110592691000_ref52","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1038\/s41377-019-0129-y","article-title":"PhaseStain: the digital staining of label-free quantitative phase microscopy images using deep learning","volume":"8","author":"Rivenson","year":"2019","journal-title":"Light Sci Appl"},{"issue":"1","key":"2022112110592691000_ref53","doi-asserted-by":"crossref","first-page":"4884","DOI":"10.1038\/s41467-021-25221-2","article-title":"Deep learning-based transformation of H&E stained tissues into special stains","volume":"12","author":"Haan","year":"2021","journal-title":"Nat Commun"},{"key":"2022112110592691000_ref54","first-page":"1","volume-title":"2019 16th International Conference on Machine Vision Applications (MVA)","author":"Fujitani","year":"2019"},{"issue":"4","key":"2022112110592691000_ref55","doi-asserted-by":"crossref","first-page":"808","DOI":"10.1038\/s41379-020-00718-1","article-title":"A large-scale internal validation study of unsupervised virtual trichrome staining technologies on nonalcoholic steatohepatitis liver biopsies","volume":"34","author":"Levy","year":"2021","journal-title":"Mod Pathol"},{"issue":"10","key":"2022112110592691000_ref56","doi-asserted-by":"crossref","first-page":"2293","DOI":"10.1109\/TMI.2019.2899364","article-title":"Generative adversarial networks for facilitating stain-independent supervised and unsupervised segmentation: a study on kidney histology","volume":"38","author":"Gadermayr","year":"2019","journal-title":"IEEE Trans Med Imaging"},{"key":"2022112110592691000_ref57","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1038\/s41377-020-0315-y","article-title":"Digital synthesis of histological stains using micro-structured and multiplexed virtual staining of label-free tissue","volume":"9","author":"Zhang","year":"2020","journal-title":"Light Sci Appl"},{"key":"2022112110592691000_ref58","doi-asserted-by":"crossref","first-page":"619803","DOI":"10.3389\/fonc.2020.619803","article-title":"Deep convolutional neural network-based lymph node metastasis prediction for colon cancer using histopathological images","volume":"10","author":"Kwak","year":"2021","journal-title":"Front Oncol"},{"key":"2022112110592691000_ref59","doi-asserted-by":"crossref","first-page":"593211","DOI":"10.3389\/fonc.2020.593211","article-title":"Predicting metastasis risk in pancreatic neuroendocrine tumors using deep learning image analysis","volume":"10","author":"Klimov","year":"2021","journal-title":"Front Oncol"},{"issue":"4","key":"2022112110592691000_ref60","doi-asserted-by":"crossref","first-page":"868","DOI":"10.1007\/s10120-021-01158-9","article-title":"Deep learning system for lymph node quantification and metastatic cancer identification from whole-slide pathology images","volume":"24","author":"Hu","year":"2021","journal-title":"Gastric Cancer"},{"issue":"8","key":"2022112110592691000_ref61","doi-asserted-by":"crossref","first-page":"1948","DOI":"10.1109\/TMI.2019.2891305","article-title":"Fast ScanNet: fast and dense analysis of multi-gigapixel whole-slide images for cancer metastasis detection","volume":"38","author":"Lin","year":"2019","journal-title":"IEEE Trans Med Imaging"},{"issue":"2","key":"2022112110592691000_ref62","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1016\/j.eururo.2020.04.023","article-title":"Deep learning predicts molecular subtype of muscle-invasive bladder cancer from conventional histopathological slides","volume":"78","author":"Woerl","year":"2020","journal-title":"Eur Urol"},{"issue":"4","key":"2022112110592691000_ref63","doi-asserted-by":"crossref","first-page":"1131","DOI":"10.1158\/1078-0432.CCR-20-3596","article-title":"Deep learning predicts HPV association in oropharyngeal squamous cell carcinomas and identifies patients with a favorable prognosis using regular H&E stains","volume":"27","author":"Klein","year":"2021","journal-title":"Clin Cancer Res"},{"issue":"24","key":"2022112110592691000_ref64","doi-asserted-by":"crossref","first-page":"11080","DOI":"10.7150\/thno.49864","article-title":"Development and interpretation of a pathomics-based model for the prediction of microsatellite instability in colorectal cancer","volume":"10","author":"Cao","year":"2020","journal-title":"Theranostics"},{"issue":"7","key":"2022112110592691000_ref65","doi-asserted-by":"crossref","first-page":"1054","DOI":"10.1038\/s41591-019-0462-y","article-title":"Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer","volume":"25","author":"Kather","year":"2019","journal-title":"Nat Med"},{"issue":"4","key":"2022112110592691000_ref66","doi-asserted-by":"crossref","first-page":"1406","DOI":"10.1053\/j.gastro.2020.06.021","article-title":"Clinical-grade detection of microsatellite instability in colorectal tumors by deep learning","volume":"159","author":"Echle","year":"2020","journal-title":"Gastroenterology"},{"issue":"1","key":"2022112110592691000_ref67","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/S1470-2045(20)30535-0","article-title":"Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study","volume":"22","author":"Yamashita","year":"2021","journal-title":"Lancet Oncol"},{"key":"2022112110592691000_ref68","doi-asserted-by":"crossref","DOI":"10.1093\/bioinformatics\/btab380","article-title":"HEAL: an automated deep learning framework for cancer histopathology image analysis","volume":"37","author":"Wang","year":"2021","journal-title":"Bioinformatics"},{"key":"2022112110592691000_ref69","doi-asserted-by":"crossref","first-page":"665929","DOI":"10.3389\/fonc.2021.665929","article-title":"Automatic pancreatic ductal adenocarcinoma detection in whole slide images using deep convolutional neural networks","volume":"11","author":"Fu","year":"2021","journal-title":"Front Oncol"},{"issue":"12","key":"2022112110592691000_ref70","doi-asserted-by":"crossref","DOI":"10.3390\/cancers11121901","article-title":"Parallel structure deep neural network using CNN and RNN with an attention mechanism for breast cancer histology image classification","volume":"11","author":"Yao","year":"2019","journal-title":"Cancers (Basel)"},{"key":"2022112110592691000_ref71","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1038\/s41746-020-0272-0","article-title":"Accurate diagnosis of lymphoma on whole-slide histopathology images using deep learning","volume":"3","author":"Syrykh","year":"2020","journal-title":"NPJ Digit Med"},{"issue":"3","key":"2022112110592691000_ref72","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1158\/1078-0432.CCR-20-3159","article-title":"A prospective validation and observer performance study of a deep learning algorithm for pathologic diagnosis of gastric tumors in endoscopic biopsies","volume":"27","author":"Park","year":"2021","journal-title":"Clin Cancer Res"},{"issue":"1","key":"2022112110592691000_ref73","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1186\/s12916-021-01953-2","article-title":"Deep learning-based six-type classifier for lung cancer and mimics from histopathological whole slide images: a retrospective study","volume":"19","author":"Yang","year":"2021","journal-title":"BMC Med"},{"issue":"2","key":"2022112110592691000_ref74","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/S1470-2045(19)30739-9","article-title":"Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study","volume":"21","author":"Bulten","year":"2020","journal-title":"Lancet Oncol"},{"issue":"2","key":"2022112110592691000_ref75","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/S1470-2045(19)30738-7","article-title":"Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study","volume":"21","author":"Str\u00f6m","year":"2020","journal-title":"Lancet Oncol"},{"issue":"2","key":"2022112110592691000_ref76","doi-asserted-by":"crossref","DOI":"10.3390\/cancers12020507","article-title":"Successful identification of nasopharyngeal carcinoma in nasopharyngeal biopsies using deep learning","volume":"12","author":"Chuang","year":"2020","journal-title":"Cancers"},{"issue":"8","key":"2022112110592691000_ref77","doi-asserted-by":"crossref","DOI":"10.3390\/cancers12082031","article-title":"Histopathological classification of breast cancer images using a multi-scale input and multi-feature network","volume":"12","author":"Sheikh","year":"2020","journal-title":"Cancers"},{"issue":"10","key":"2022112110592691000_ref78","doi-asserted-by":"crossref","DOI":"10.3390\/cancers13102419","article-title":"Deep learning for the classification of non-Hodgkin lymphoma on histopathological images","volume":"13","author":"Steinbuss","year":"2021","journal-title":"Cancers"},{"issue":"3","key":"2022112110592691000_ref79","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1038\/s41379-020-0640-y","article-title":"Artificial intelligence assistance significantly improves Gleason grading of prostate biopsies by pathologists","volume":"34","author":"Bulten","year":"2021","journal-title":"Mod Pathol"},{"issue":"8","key":"2022112110592691000_ref80","doi-asserted-by":"crossref","first-page":"1588","DOI":"10.1038\/s41379-021-00794-x","article-title":"An independent assessment of an artificial intelligence system for prostate cancer detection shows strong diagnostic accuracy","volume":"34","author":"Perincheri","year":"2021","journal-title":"Mod Pathol"},{"key":"2022112110592691000_ref81","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.media.2018.09.005","article-title":"Automatic grading of prostate cancer in digitized histopathology images: learning from multiple experts","volume":"50","author":"Nir","year":"2018","journal-title":"Med Image Anal"},{"issue":"6","key":"2022112110592691000_ref82","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1038\/s41551-020-00682-w","article-title":"Data-efficient and weakly supervised computational pathology on whole-slide images","volume":"5","author":"Lu","year":"2021","journal-title":"Nat Biomed Eng"},{"key":"2022112110592691000_ref83","doi-asserted-by":"crossref","first-page":"101549","DOI":"10.1016\/j.media.2019.101549","article-title":"RMDL: recalibrated multi-instance deep learning for whole slide gastric image classification","volume":"58","author":"Wang","year":"2019","journal-title":"Med Image Anal"},{"key":"2022112110592691000_ref84","doi-asserted-by":"crossref","first-page":"101814","DOI":"10.1016\/j.media.2020.101814","article-title":"Weakly supervised instance learning for thyroid malignancy prediction from whole slide cytopathology images","volume":"67","author":"Dov","year":"2021","journal-title":"Med Image Anal"},{"issue":"1","key":"2022112110592691000_ref85","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1186\/s12916-021-01942-5","article-title":"Accurate diagnosis of colorectal cancer based on histopathology images using artificial intelligence","volume":"19","author":"Wang","year":"2021","journal-title":"BMC Med"},{"issue":"2","key":"2022112110592691000_ref86","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1007\/s10120-017-0731-8","article-title":"Automated histological classification of whole-slide images of gastric biopsy specimens","volume":"21","author":"Yoshida","year":"2018","journal-title":"Gastric Cancer"},{"issue":"1","key":"2022112110592691000_ref87","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1109\/TMI.2017.2758580","article-title":"Multi-instance multi-label learning for multi-class classification of whole slide breast histopathology images","volume":"37","author":"Mercan","year":"2018","journal-title":"IEEE Trans Med Imaging"},{"issue":"9","key":"2022112110592691000_ref88","doi-asserted-by":"crossref","first-page":"3950","DOI":"10.1109\/TCYB.2019.2935141","article-title":"Weakly supervised deep learning for whole slide lung cancer image analysis","volume":"50","author":"Wang","year":"2020","journal-title":"IEEE Trans Cybern"},{"key":"2022112110592691000_ref89","doi-asserted-by":"crossref","first-page":"102167","DOI":"10.1016\/j.media.2021.102167","article-title":"Computer-aided diagnosis tool for cervical cancer screening with weakly supervised localization and detection of abnormalities using adaptable and explainable classifier","volume":"73","author":"Pirovano","year":"2021","journal-title":"Med Image Anal"},{"issue":"7","key":"2022112110592691000_ref90","doi-asserted-by":"crossref","first-page":"1817","DOI":"10.1109\/TMI.2021.3066295","article-title":"Detection of prostate cancer in whole-slide images through end-to-end training with image-level labels","volume":"40","author":"Pinckaers","year":"2021","journal-title":"IEEE Trans Med Imaging"},{"issue":"8","key":"2022112110592691000_ref91","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1038\/s41591-019-0508-1","article-title":"Clinical-grade computational pathology using weakly supervised deep learning on whole slide images","volume":"25","author":"Campanella","year":"2019","journal-title":"Nat Med"},{"issue":"1","key":"2022112110592691000_ref92","doi-asserted-by":"crossref","first-page":"6004","DOI":"10.1038\/s41467-020-19817-3","article-title":"A deep learning diagnostic platform for diffuse large B-cell lymphoma with high accuracy across multiple hospitals","volume":"11","author":"Li","year":"2020","journal-title":"Nat Commun"},{"key":"2022112110592691000_ref93","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.media.2017.04.008","article-title":"A structured latent model for ovarian carcinoma subtyping from histopathology slides","volume":"39","author":"BenTaieb","year":"2017","journal-title":"Med Image Anal"},{"key":"2022112110592691000_ref94","doi-asserted-by":"crossref","first-page":"12474","DOI":"10.1038\/ncomms12474","article-title":"Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features","volume":"7","author":"Yu","year":"2016","journal-title":"Nat Commun"},{"issue":"5","key":"2022112110592691000_ref95","doi-asserted-by":"crossref","first-page":"1126","DOI":"10.1158\/1078-0432.CCR-19-1495","article-title":"Deep learning based on standard H&E images of primary melanoma tumors identifies patients at risk for visceral recurrence and death","volume":"26","author":"Kulkarni","year":"2020","journal-title":"Clin Cancer Res"},{"issue":"12","key":"2022112110592691000_ref96","doi-asserted-by":"crossref","DOI":"10.3390\/cancers13123050","article-title":"Automated quantification of sTIL density with H&E-based digital image analysis has prognostic potential in triple-negative breast cancers","volume":"13","author":"Thagaard","year":"2021","journal-title":"Cancers"},{"issue":"8","key":"2022112110592691000_ref97","doi-asserted-by":"crossref","DOI":"10.1172\/JCI145488","article-title":"Computerized tumor multinucleation index (MuNI) is prognostic in p16+ oropharyngeal carcinoma","volume":"131","author":"Koyuncu","year":"2021","journal-title":"J Clin Invest"},{"issue":"1","key":"2022112110592691000_ref98","doi-asserted-by":"crossref","first-page":"e1002730","DOI":"10.1371\/journal.pmed.1002730","article-title":"Predicting survival from colorectal cancer histology slides using deep learning: a retrospective multicenter study","volume":"16","author":"Kather","year":"2019","journal-title":"PLoS Med"},{"issue":"10","key":"2022112110592691000_ref99","doi-asserted-by":"crossref","first-page":"1519","DOI":"10.1038\/s41591-019-0583-3","article-title":"Deep learning-based classification of mesothelioma improves prediction of patient outcome","volume":"25","author":"Courtiol","year":"2019","journal-title":"Nat Med"},{"issue":"10","key":"2022112110592691000_ref100","doi-asserted-by":"crossref","DOI":"10.3390\/cancers11101579","article-title":"Prediction of BAP1 expression in uveal melanoma using densely-connected deep classification networks","volume":"11","author":"Sun","year":"2019","journal-title":"Cancers"},{"key":"2022112110592691000_ref101","doi-asserted-by":"crossref","first-page":"101789","DOI":"10.1016\/j.media.2020.101789","article-title":"Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks","volume":"65","author":"Yao","year":"2020","journal-title":"Med Image Anal"},{"issue":"10221","key":"2022112110592691000_ref102","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/S0140-6736(19)32998-8","article-title":"Deep learning for prediction of colorectal cancer outcome: a discovery and validation study","volume":"395","author":"Skrede","year":"2020","journal-title":"The Lancet"},{"issue":"5","key":"2022112110592691000_ref103","doi-asserted-by":"crossref","first-page":"951","DOI":"10.1136\/gutjnl-2020-320930","article-title":"Exploring prognostic indicators in the pathological images of hepatocellular carcinoma based on deep learning","volume":"70","author":"Shi","year":"2021","journal-title":"Gut"},{"issue":"6","key":"2022112110592691000_ref104","doi-asserted-by":"crossref","DOI":"10.3390\/cancers13061325","article-title":"The potential of artificial intelligence to detect lymphovascular invasion in testicular cancer","volume":"13","author":"Ghosh","year":"2021","journal-title":"Cancers"},{"issue":"5","key":"2022112110592691000_ref105","doi-asserted-by":"crossref","first-page":"1526","DOI":"10.1158\/1078-0432.CCR-18-2013","article-title":"Spatial architecture and arrangement of tumor-infiltrating lymphocytes for predicting likelihood of recurrence in early-stage non-small cell lung cancer","volume":"25","author":"Corredor","year":"2019","journal-title":"Clin Cancer Res"},{"issue":"2","key":"2022112110592691000_ref106","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1038\/s41379-020-00671-z","article-title":"Prediction of early recurrence of hepatocellular carcinoma after resection using digital pathology images assessed by machine learning","volume":"34","author":"Saito","year":"2021","journal-title":"Mod Pathol"},{"issue":"13","key":"2022112110592691000_ref107","doi-asserted-by":"crossref","DOI":"10.3390\/cancers13133308","article-title":"DeepRePath: identifying the prognostic features of early-stage lung adenocarcinoma using multi-scale pathology images and deep convolutional neural networks","volume":"13","author":"Shim","year":"2021","journal-title":"Cancers"},{"issue":"9","key":"2022112110592691000_ref108","doi-asserted-by":"crossref","first-page":"1178","DOI":"10.1016\/j.annonc.2021.06.007","article-title":"Deep learning for diagnosis and survival prediction in soft tissue sarcoma","volume":"32","author":"Foersch","year":"2021","journal-title":"Ann Oncol"},{"issue":"1","key":"2022112110592691000_ref109","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.annonc.2021.09.007","article-title":"Improved breast cancer histological grading using deep learning","volume":"33","author":"Wang","year":"2022","journal-title":"Ann Oncol"},{"issue":"3","key":"2022112110592691000_ref110","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1038\/s41416-021-01394-x","article-title":"A deep convolutional neural network for segmentation of whole-slide pathology images identifies novel tumor cell-perivascular niche interactions that are associated with poor survival in glioblastoma","volume":"125","author":"Zadeh Shirazi","year":"2021","journal-title":"Br J Cancer"},{"issue":"1","key":"2022112110592691000_ref111","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1186\/s12916-020-01684-w","article-title":"Deciphering serous ovarian carcinoma histopathology and platinum response by convolutional neural networks","volume":"18","author":"Yu","year":"2020","journal-title":"BMC Med"},{"issue":"7","key":"2022112110592691000_ref112","doi-asserted-by":"crossref","DOI":"10.3390\/cancers13071615","article-title":"Automated detection and classification of desmoplastic reaction at the colorectal tumour front using deep learning","volume":"13","author":"Nearchou","year":"2021","journal-title":"Cancers"},{"issue":"1","key":"2022112110592691000_ref113","doi-asserted-by":"crossref","first-page":"1637","DOI":"10.1038\/s41467-021-21674-7","article-title":"Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning","volume":"12","author":"Wang","year":"2021","journal-title":"Nat Commun"},{"issue":"3","key":"2022112110592691000_ref114","doi-asserted-by":"crossref","first-page":"562","DOI":"10.1038\/s41379-020-00686-6","article-title":"Optimization of an automated tumor-infiltrating lymphocyte algorithm for improved prognostication in primary melanoma","volume":"34","author":"Chou","year":"2021","journal-title":"Mod Pathol"},{"issue":"1","key":"2022112110592691000_ref115","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1038\/s41523-020-00205-5","article-title":"Unmasking the immune microecology of ductal carcinoma in situ with deep learning","volume":"7","author":"Narayanan","year":"2021","journal-title":"NPJ Breast Cancer"},{"issue":"1","key":"2022112110592691000_ref116","doi-asserted-by":"crossref","first-page":"5440","DOI":"10.1038\/s41467-019-13043-2","article-title":"An open source automated tumor infiltrating lymphocyte algorithm for prognosis in melanoma","volume":"10","author":"Acs","year":"2019","journal-title":"Nat Commun"},{"issue":"2","key":"2022112110592691000_ref117","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1093\/jnci\/djx137","article-title":"Relevance of spatial heterogeneity of immune infiltration for predicting risk of recurrence after endocrine therapy of ER+ breast cancer","volume":"110","author":"Heindl","year":"2018","journal-title":"J Natl Cancer Inst"},{"issue":"8","key":"2022112110592691000_ref118","doi-asserted-by":"crossref","first-page":"1915","DOI":"10.1158\/1078-0432.CCR-19-2659","article-title":"Computationally derived image signature of stromal morphology is prognostic of prostate cancer recurrence following prostatectomy in African American patients","volume":"26","author":"Bhargava","year":"2020","journal-title":"Clin Cancer Res"},{"key":"2022112110592691000_ref119","article-title":"DeepOmix: A scalable and interpretable multi-omics deep learning framework and application in cancer survival analysis","volume":"19","author":"","journal-title":"Computational and structural biotechnology journal"},{"issue":"2","key":"2022112110592691000_ref120","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1038\/s42256-021-00432-w","article-title":"Cell type annotation of single-cell chromatin accessibility data via supervised Bayesian embedding","volume":"4","author":"Chen","year":"2022","journal-title":"Nat Mach Intell"},{"issue":"6","key":"2022112110592691000_ref121","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1038\/s42256-021-00333-y","article-title":"Simultaneous deep generative modeling and clustering of single cell genomic data","volume":"3","author":"Liu","year":"2021","journal-title":"Nat Mach Intell"},{"issue":"1","key":"2022112110592691000_ref122","article-title":"RA3 is a reference-guided approach for epigenetic characterization of single cells","volume":"12","author":"Chen","year":"2021","journal-title":"Nat Commun"},{"issue":"15","key":"2022112110592691000_ref123","doi-asserted-by":"crossref","DOI":"10.1073\/pnas.2101344118","article-title":"Density estimation using deep generative neural networks","volume":"118","author":"Liu","year":"2021","journal-title":"Proc Natl Acad Sci U S A"},{"issue":"19","key":"2022112110592691000_ref124","doi-asserted-by":"crossref","DOI":"10.3390\/cancers13194837","article-title":"Morphological features extracted by AI associated with spatial transcriptomics in prostate cancer","volume":"13","author":"Chelebian","year":"2021","journal-title":"Cancers"},{"issue":"3","key":"2022112110592691000_ref125","doi-asserted-by":"crossref","first-page":"544","DOI":"10.1136\/gutjnl-2019-319866","article-title":"Image-based consensus molecular subtype (imCMS) classification of colorectal cancer using deep learning","volume":"70","author":"Sirinukunwattana","year":"2021","journal-title":"Gut"},{"issue":"1","key":"2022112110592691000_ref126","doi-asserted-by":"crossref","first-page":"1613","DOI":"10.1038\/s41467-021-21896-9","article-title":"Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes","volume":"12","author":"Diao","year":"2021","journal-title":"Nat Commun"},{"issue":"1","key":"2022112110592691000_ref127","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1093\/neuonc\/noaa163","article-title":"Artificial intelligence neuropathologist for glioma classification using deep learning on hematoxylin and eosin stained slide images and molecular markers","volume":"23","author":"Jin","year":"2021","journal-title":"Neuro Oncol"},{"issue":"1","key":"2022112110592691000_ref128","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1038\/s41698-021-00225-9","article-title":"Genetic mutation and biological pathway prediction based on whole slide images in breast carcinoma using deep learning","volume":"5","author":"Qu","year":"2021","journal-title":"NPJ Precis Oncol"},{"issue":"12","key":"2022112110592691000_ref129","doi-asserted-by":"crossref","DOI":"10.3390\/cancers12123687","article-title":"Deep learning predicts underlying features on pathology images with therapeutic relevance for breast and gastric cancer","volume":"12","author":"Valieris","year":"2020","journal-title":"Cancers"},{"key":"2022112110592691000_ref130","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1038\/s41525-020-0120-9","article-title":"Whole slide images reflect DNA methylation patterns of human tumors","volume":"5","author":"Zheng","year":"2020","journal-title":"NPJ Genom Med"},{"key":"2022112110592691000_ref131","doi-asserted-by":"crossref","first-page":"103388","DOI":"10.1016\/j.ebiom.2021.103388","article-title":"Deep learning predicts gene expression as an intermediate data modality to identify susceptibility patterns in Mycobacterium tuberculosis infected diversity outbred mice","volume":"67","author":"Tavolara","year":"2021","journal-title":"EBioMedicine"},{"key":"2022112110592691000_ref132","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1038\/s41698-020-0120-3","article-title":"Classification and mutation prediction based on histopathology H&E images in liver cancer using deep learning","volume":"4","author":"Chen","year":"2020","journal-title":"NPJ Precis Oncol"},{"key":"2022112110592691000_ref133","doi-asserted-by":"crossref","first-page":"642945","DOI":"10.3389\/fonc.2021.642945","article-title":"Prediction of target-drug therapy by identifying gene mutations in lung cancer with histopathological stained image and deep learning techniques","volume":"11","author":"Huang","year":"2021","journal-title":"Front Oncol"},{"issue":"10","key":"2022112110592691000_ref134","doi-asserted-by":"crossref","first-page":"1559","DOI":"10.1038\/s41591-018-0177-5","article-title":"Classification and mutation prediction from non\u2013small cell lung cancer histopathology images using deep learning","volume":"24","author":"Coudray","year":"2018","journal-title":"Nat Med"},{"issue":"1","key":"2022112110592691000_ref135","doi-asserted-by":"crossref","first-page":"6367","DOI":"10.1038\/s41467-020-20030-5","article-title":"Deep learning-based cross-classifications reveal conserved spatial behaviors within tumor histological images","volume":"11","author":"Noorbakhsh","year":"2020","journal-title":"Nat Commun"},{"issue":"7","key":"2022112110592691000_ref136","doi-asserted-by":"crossref","DOI":"10.3390\/cancers13071659","article-title":"ImmunoAIzer: a deep learning-based computational framework to characterize cell distribution and gene mutation in tumor microenvironment","volume":"13","author":"Bian","year":"2021","journal-title":"Cancers"},{"issue":"13","key":"2022112110592691000_ref137","doi-asserted-by":"crossref","first-page":"E2970","DOI":"10.1073\/pnas.1717139115","article-title":"Predicting cancer outcomes from histology and genomics using convolutional networks","volume":"115","author":"Mobadersany","year":"2018","journal-title":"Proc Natl Acad Sci U S A"},{"key":"2022112110592691000_ref138","first-page":"4015","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Chen"},{"key":"2022112110592691000_ref139","doi-asserted-by":"crossref","first-page":"1758835920971416","DOI":"10.1177\/1758835920971416","article-title":"A deep-learning-based prognostic nomogram integrating microscopic digital pathology and macroscopic magnetic resonance images in nasopharyngeal carcinoma: a multi-cohort study","volume":"12","author":"Zhang","year":"2020","journal-title":"Ther Adv Med Oncol"},{"issue":"1","key":"2022112110592691000_ref140","doi-asserted-by":"crossref","first-page":"5727","DOI":"10.1038\/s41467-020-19334-3","article-title":"Deep learning-enabled breast cancer hormonal receptor status determination from base-level H&E stains","volume":"11","author":"Naik","year":"2020","journal-title":"Nat Commun"},{"key":"2022112110592691000_ref141","doi-asserted-by":"crossref","first-page":"596499","DOI":"10.3389\/fonc.2021.596499","article-title":"Histological severity risk factors identification in juvenile-onset recurrent respiratory papillomatosis: how immunohistochemistry and AI algorithms can help?","volume":"11","author":"Lepine","year":"2021","journal-title":"Front Oncol"},{"issue":"9","key":"2022112110592691000_ref142","doi-asserted-by":"crossref","first-page":"1780","DOI":"10.1038\/s41379-021-00826-6","article-title":"Artificial intelligence for advance requesting of immunohistochemistry in diagnostically uncertain prostate biopsies","volume":"34","author":"Chatrian","year":"2021","journal-title":"Mod Pathol"},{"issue":"5","key":"2022112110592691000_ref143","doi-asserted-by":"crossref","first-page":"1344","DOI":"10.3390\/cancers12051344","article-title":"A machine-learning approach for the assessment of the proliferative compartment of solid tumors on hematoxylin-eosin-stained sections","volume":"12","author":"Martino","year":"2020","journal-title":"Cancers (Basel)"},{"issue":"1","key":"2022112110592691000_ref144","doi-asserted-by":"crossref","first-page":"3877","DOI":"10.1038\/s41467-020-17678-4","article-title":"A deep learning model to predict RNA-Seq expression of tumours from whole slide images","volume":"11","author":"Schmauch","year":"2020","journal-title":"Nat Commun"},{"issue":"7","key":"2022112110592691000_ref145","doi-asserted-by":"crossref","first-page":"1054","DOI":"10.1038\/s41591-020-0900-x","article-title":"Geospatial immune variability illuminates differential evolution of lung adenocarcinoma","volume":"26","author":"AbdulJabbar","year":"2020","journal-title":"Nat Med"},{"key":"2022112110592691000_ref146","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1038\/s41523-018-0084-4","article-title":"Correlating nuclear morphometric patterns with estrogen receptor status in breast cancer pathologic specimens","volume":"4","author":"Rawat","year":"2018","journal-title":"NPJ Breast Cancer"},{"key":"2022112110592691000_ref147","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.media.2018.07.004","article-title":"Segmentation of glandular epithelium in colorectal tumours to automatically compartmentalise IHC biomarker quantification: a deep learning approach","volume":"49","author":"Van Eycke","year":"2018","journal-title":"Med Image Anal"},{"issue":"6","key":"2022112110592691000_ref148","doi-asserted-by":"crossref","first-page":"1041","DOI":"10.1007\/s10120-020-01093-1","article-title":"Development and validation of a deep learning system for ascites cytopathology interpretation","volume":"23","author":"Su","year":"2020","journal-title":"Gastric Cancer"},{"issue":"4","key":"2022112110592691000_ref149","doi-asserted-by":"crossref","DOI":"10.3390\/cancers13040617","article-title":"PathoFusion: an open-source AI framework for recognition of pathomorphological features and mapping of immunohistochemical data","volume":"13","author":"Bao","year":"2021","journal-title":"Cancers"},{"issue":"6","key":"2022112110592691000_ref150","doi-asserted-by":"crossref","first-page":"3061","DOI":"10.1021\/acs.analchem.0c02726","article-title":"Deep learning-based annotation transfer between molecular imaging modalities: an automated workflow for multimodal data integration","volume":"93","author":"Race","year":"2021","journal-title":"Anal Chem"},{"issue":"7","key":"2022112110592691000_ref151","doi-asserted-by":"crossref","first-page":"1405","DOI":"10.1109\/TMI.2017.2677479","article-title":"Automatic quantification of tumour hypoxia from multi-modal microscopy images using weakly-supervised learning methods","volume":"36","author":"Carneiro","year":"2017","journal-title":"IEEE Trans Med Imaging"},{"issue":"1","key":"2022112110592691000_ref152","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.kint.2020.07.044","article-title":"Development and evaluation of deep learning-based segmentation of histologic structures in the kidney cortex with multiple histologic stains","volume":"99","author":"Jayapandian","year":"2021","journal-title":"Kidney Int"},{"issue":"5","key":"2022112110592691000_ref153","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1038\/s41591-021-01287-9","article-title":"Triage-driven diagnosis of Barrett's esophagus for early detection of esophageal adenocarcinoma using deep learning","volume":"27","author":"Gehrung","year":"2021","journal-title":"Nat Med"},{"issue":"3","key":"2022112110592691000_ref154","doi-asserted-by":"crossref","DOI":"10.3390\/cancers12030578","article-title":"Glioma grading via analysis of digital pathology images using machine learning","volume":"12","author":"Rathore","year":"2020","journal-title":"Cancers"},{"issue":"3","key":"2022112110592691000_ref155","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1002\/ijc.33288","article-title":"Clinical use of a machine learning histopathological image signature in diagnosis and survival prediction of clear cell renal cell carcinoma","volume":"148","author":"Chen","year":"2021","journal-title":"Int J Cancer"},{"key":"2022112110592691000_ref156","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.ejca.2021.05.026","article-title":"Deep learning approach to predict sentinel lymph node status directly from routine histology of primary melanoma tumours","volume":"154","author":"Brinker","year":"2021","journal-title":"Eur J Cancer"},{"key":"2022112110592691000_ref157","doi-asserted-by":"crossref","first-page":"668694","DOI":"10.3389\/fonc.2021.668694","article-title":"Deep neural network analysis of pathology images with integrated molecular data for enhanced glioma classification and grading","volume":"11","author":"Pei","year":"2021","journal-title":"Front Oncol"},{"issue":"6","key":"2022112110592691000_ref158","doi-asserted-by":"crossref","first-page":"620","DOI":"10.1016\/j.cels.2017.10.014","article-title":"Association of omics features with histopathology patterns in lung adenocarcinoma","volume":"5","author":"Yu","year":"2017","journal-title":"Cell Syst"},{"key":"2022112110592691000_ref159","doi-asserted-by":"crossref","first-page":"101563","DOI":"10.1016\/j.media.2019.101563","article-title":"Hover-net: simultaneous segmentation and classification of nuclei in multi-tissue histology images","volume":"58","author":"Graham","year":"2019","journal-title":"Med Image Anal"},{"key":"2022112110592691000_ref160","doi-asserted-by":"crossref","first-page":"101914","DOI":"10.1016\/j.media.2020.101914","article-title":"A hybrid network for automatic hepatocellular carcinoma segmentation in H&E-stained whole slide images","volume":"68","author":"Wang","year":"2021","journal-title":"Med Image Anal"},{"issue":"12","key":"2022112110592691000_ref161","doi-asserted-by":"crossref","DOI":"10.3390\/cancers12123562","article-title":"Single-cell spatial analysis of tumor and immune microenvironment on whole-slide image reveals hepatocellular carcinoma subtypes","volume":"12","author":"Wang","year":"2020","journal-title":"Cancers"},{"key":"2022112110592691000_ref162","doi-asserted-by":"crossref","first-page":"101912","DOI":"10.1016\/j.media.2020.101912","article-title":"Learning to segment images with classification labels","volume":"68","author":"Ciga","year":"2021","journal-title":"Med Image Anal"},{"issue":"1","key":"2022112110592691000_ref163","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1002\/path.5800","article-title":"Weakly supervised annotation-free cancer detection and prediction of genotype in routine histopathology","volume":"256","author":"Schrammen","year":"2022","journal-title":"J Pathol"},{"issue":"7861","key":"2022112110592691000_ref164","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1038\/s41586-021-03512-4","article-title":"AI-based pathology predicts origins for cancers of unknown primary","volume":"594","author":"Lu","year":"2021","journal-title":"Nature"},{"key":"2022112110592691000_ref165","doi-asserted-by":"crossref","first-page":"102032","DOI":"10.1016\/j.media.2021.102032","article-title":"Fine-tuning and training of densenet for histopathology image representation using TCGA diagnostic slides","volume":"70","author":"Riasatian","year":"2021","journal-title":"Med Image Anal"},{"key":"2022112110592691000_ref166","doi-asserted-by":"crossref","first-page":"101757","DOI":"10.1016\/j.media.2020.101757","article-title":"Yottixel - an image search engine for large archives of histopathology whole slide images","volume":"65","author":"Kalra","year":"2020","journal-title":"Med Image Anal"},{"issue":"2","key":"2022112110592691000_ref167","doi-asserted-by":"crossref","first-page":"188520","DOI":"10.1016\/j.bbcan.2021.188520","article-title":"Artificial intelligence and digital pathology: opportunities and implications for immuno-oncology","volume":"1875","author":"Sobhani","year":"2021","journal-title":"Biochim Biophys Acta Rev Cancer"},{"key":"2022112110592691000_ref168","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.semcancer.2020.08.006","article-title":"Deep computational pathology in breast cancer","volume":"72","author":"Duggento","year":"2021","journal-title":"Semin Cancer Biol"},{"issue":"7216","key":"2022112110592691000_ref169","doi-asserted-by":"crossref","first-page":"1061","DOI":"10.1038\/nature07385","article-title":"Comprehensive genomic characterization defines human glioblastoma genes and core pathways","volume":"455","author":"Network, C.G.A.R","year":"2008","journal-title":"Nature"},{"key":"2022112110592691000_ref170","doi-asserted-by":"crossref","first-page":"8","DOI":"10.4103\/2153-3539.112693","article-title":"Mitosis detection in breast cancer histological images an ICPR 2012 contest","volume":"4","author":"Roux","year":"2013","journal-title":"J Pathol Inform"},{"key":"2022112110592691000_ref171","volume-title":"Image Pervasive Access Lab (IPAL), Agency Sci., Technol. & Res. Inst. Infocom Res., Singapore, Tech. Rep","author":"","year":"2014"},{"key":"2022112110592691000_ref172","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.media.2016.08.008","article-title":"Gland segmentation in colon histology images: the glas challenge contest","volume":"35","author":"Sirinukunwattana","year":"2017","journal-title":"Med Image Anal"},{"issue":"22","key":"2022112110592691000_ref173","doi-asserted-by":"crossref","first-page":"2199","DOI":"10.1001\/jama.2017.14585","article-title":"Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer","volume":"318","author":"Ehteshami Bejnordi","year":"2017","journal-title":"JAMA"},{"key":"2022112110592691000_ref174","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.media.2019.02.012","article-title":"Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge","volume":"54","author":"Veta","year":"2019","journal-title":"Med Image Anal"},{"issue":"6","key":"2022112110592691000_ref175","doi-asserted-by":"crossref","DOI":"10.1093\/gigascience\/giy065","article-title":"1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset","volume":"7","author":"Litjens","year":"2018","journal-title":"Gigascience"},{"key":"2022112110592691000_ref176","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.media.2019.05.010","article-title":"BACH: grand challenge on breast cancer histology images","volume":"56","author":"Aresta","year":"2019","journal-title":"Med Image Anal"},{"key":"2022112110592691000_ref177","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1007\/978-3-030-00934-2_24","volume-title":"Medical Image Computing and Computer Assisted Intervention - Miccai 2018, Pt Ii","author":"Veeling","year":"2018"},{"issue":"10","key":"2022112110592691000_ref178","doi-asserted-by":"crossref","first-page":"3042","DOI":"10.1109\/TMI.2020.2986331","article-title":"ANHIR: automatic non-rigid histological image registration challenge","volume":"39","author":"Borovec","year":"2020","journal-title":"IEEE Trans Med Imaging"},{"issue":"18","key":"2022112110592691000_ref179","doi-asserted-by":"crossref","first-page":"3461","DOI":"10.1093\/bioinformatics\/btz083","article-title":"Structured crowdsourcing enables convolutional segmentation of histology images","volume":"35","author":"Amgad","year":"2019","journal-title":"Bioinformatics"},{"key":"2022112110592691000_ref180","doi-asserted-by":"crossref","first-page":"842","DOI":"10.1007\/978-3-030-20351-1_66","volume-title":"Information Processing in Medical Imaging, IPMI","author":"Li","year":"2019"},{"key":"2022112110592691000_ref181","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1038\/s41597-019-0290-4","article-title":"A large-scale dataset for mitotic figure assessment on whole slide images of canine cutaneous mast cell tumor","volume":"6","author":"Bertram","year":"2019","journal-title":"Sci Data"},{"key":"2022112110592691000_ref182","doi-asserted-by":"crossref","first-page":"101854","DOI":"10.1016\/j.media.2020.101854","article-title":"PAIP 2019: liver cancer segmentation challenge","volume":"67","author":"Kim","year":"2021","journal-title":"Med Image Anal"},{"key":"2022112110592691000_ref183","article-title":"HEROHE challenge: assessing HER2 status in breast cancer without immunohistochemistry or in situ hybridization","author":"Conde-Sousa","year":"2021"},{"issue":"1","key":"2022112110592691000_ref184","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1038\/s41597-020-00756-z","article-title":"A completely annotated whole slide image dataset of canine breast cancer to aid human breast cancer research","volume":"7","author":"Aubreville","year":"2020","journal-title":"Sci Data"},{"issue":"12","key":"2022112110592691000_ref185","doi-asserted-by":"crossref","first-page":"3413","DOI":"10.1109\/TMI.2021.3085712","article-title":"MoNuSAC2020: a multi-organ nuclei segmentation and classification challenge","volume":"40","author":"Verma","year":"2021","journal-title":"IEEE Trans Med Imaging"},{"key":"2022112110592691000_ref186","first-page":"154","article-title":"Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge","volume-title":"Nature medicine","author":"","year":"2020"},{"key":"2022112110592691000_ref187","doi-asserted-by":"crossref","first-page":"759007","DOI":"10.3389\/fonc.2021.759007","article-title":"Predicting axillary lymph node metastasis in early breast cancer using deep learning on primary tumor biopsy slides","volume":"11","author":"Xu","year":"2021","journal-title":"Front Oncol"},{"key":"2022112110592691000_ref188","article-title":"Quantifying the scanner-induced domain gap in mitosis detection","author":"Aubreville","year":"2021"},{"key":"2022112110592691000_ref189","article-title":"NuCLS: a scalable crowdsourcing, deep learning approach and dataset for nucleus classification, localization and segmentation","author":"Amgad","year":"2021"},{"issue":"3","key":"2022112110592691000_ref190","doi-asserted-by":"crossref","first-page":"302","DOI":"10.5853\/jos.2017.02922","article-title":"Cerebral small vessel disease: a review focusing on pathophysiology, biomarkers, and machine learning strategies","volume":"20","author":"Cuadrado-Godia","year":"2018","journal-title":"J Stroke"},{"key":"2022112110592691000_ref191","article-title":"Multi-layer pseudo-supervision for histopathology tissue semantic segmentation using patch-level classification labels","volume-title":"Medical Image Analysis","author":"Han","year":"2021"},{"key":"2022112110592691000_ref192","article-title":"CoNIC: colon nuclei identification and counting challenge 2022","author":"Graham","year":"2021"},{"issue":"11","key":"2022112110592691000_ref193","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1038\/s41571-019-0252-y","article-title":"Artificial intelligence in digital pathology - new tools for diagnosis and precision oncology","volume":"16","author":"Bera","year":"2019","journal-title":"Nat Rev Clin Oncol"},{"issue":"1","key":"2022112110592691000_ref194","doi-asserted-by":"crossref","first-page":"4423","DOI":"10.1038\/s41467-021-24698-1","article-title":"The impact of site-specific digital histology signatures on deep learning model accuracy and bias","volume":"12","author":"Howard","year":"2021","journal-title":"Nat Commun"},{"issue":"1","key":"2022112110592691000_ref195","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1002\/path.5797","article-title":"The utility of color normalization for AI-based diagnosis of hematoxylin and eosin-stained pathology images","volume":"256","author":"Boschman","year":"2022","journal-title":"J Pathol"},{"key":"2022112110592691000_ref196","doi-asserted-by":"crossref","first-page":"101544","DOI":"10.1016\/j.media.2019.101544","article-title":"Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology","volume":"58","author":"Tellez","year":"2019","journal-title":"Med Image Anal"},{"key":"2022112110592691000_ref197","doi-asserted-by":"crossref","first-page":"4840","DOI":"10.1016\/j.csbj.2021.08.033","article-title":"HistoClean: open-source software for histological image pre-processing and augmentation to improve development of robust convolutional neural networks","volume":"19","author":"McCombe","year":"2021","journal-title":"Comput Struct Biotechnol J"},{"key":"2022112110592691000_ref198","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.ejca.2021.02.032","article-title":"Combining CNN-based histologic whole slide image analysis and patient data to improve skin cancer classification","volume":"149","author":"Hohn","year":"2021","journal-title":"Eur J Cancer"},{"issue":"1","key":"2022112110592691000_ref199","doi-asserted-by":"crossref","first-page":"1193","DOI":"10.1038\/s41467-021-21467-y","article-title":"An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning","volume":"12","author":"Chen","year":"2021","journal-title":"Nat Commun"},{"issue":"10","key":"2022112110592691000_ref200","doi-asserted-by":"crossref","first-page":"2058","DOI":"10.1038\/s41379-020-0551-y","article-title":"Novel artificial intelligence system increases the detection of prostate cancer in whole slide images of core needle biopsies","volume":"33","author":"Raciti","year":"2020","journal-title":"Mod Pathol"},{"key":"2022112110592691000_ref201","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.ejca.2019.06.012","article-title":"Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images","volume":"118","author":"Hekler","year":"2019","journal-title":"Eur J Cancer"},{"issue":"11","key":"2022112110592691000_ref202","doi-asserted-by":"crossref","first-page":"2115","DOI":"10.1038\/s41379-020-0601-5","article-title":"Validation of a digital pathology system including remote review during the COVID-19 pandemic","volume":"33","author":"Hanna","year":"2020","journal-title":"Mod Pathol"},{"issue":"2","key":"2022112110592691000_ref203","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1002\/path.5662","article-title":"Independent real-world application of a clinical-grade automated prostate cancer detection system","volume":"254","author":"Silva","year":"2021","journal-title":"J Pathol"},{"key":"2022112110592691000_ref204","article-title":"Imagenet: A large-scale hierarchical image database","author":"","journal-title":"2009 IEEE conference on computer vision and pattern recognition"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/23\/6\/bbac367\/47143735\/bbac367.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/23\/6\/bbac367\/47143735\/bbac367.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,21]],"date-time":"2022-11-21T11:02:36Z","timestamp":1669028556000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbac367\/6702380"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,16]]},"references-count":204,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2022,11,19]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbac367","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022,11]]},"published":{"date-parts":[[2022,9,16]]},"article-number":"bbac367"}}