{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T01:43:23Z","timestamp":1792633403145,"version":"4.5.0"},"reference-count":87,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2022,11,23]],"date-time":"2022-11-23T00:00:00Z","timestamp":1669161600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Pathology Informatics"],"published-print":{"date-parts":[[2023]]},"DOI":"10.1016\/j.jpi.2022.100159","type":"journal-article","created":{"date-parts":[[2022,11,25]],"date-time":"2022-11-25T21:46:47Z","timestamp":1669412807000},"page":"100159","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":11,"special_numbering":"C","title":["High-fidelity detection, subtyping, and localization of five skin neoplasms using supervised and semi-supervised learning"],"prefix":"10.1016","volume":"14","author":[{"given":"James","family":"Requa","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tuatini","family":"Godard","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rajni","family":"Mandal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bonnie","family":"Balzer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Darren","family":"Whittemore","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eva","family":"George","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Frenalyn","family":"Barcelona","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chalette","family":"Lambert","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonathan","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Allison","family":"Lambert","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"April","family":"Larson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gregory","family":"Osmond","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.jpi.2022.100159_bb0005","doi-asserted-by":"crossref","first-page":"4337","DOI":"10.1038\/s41598-021-83502-8","article-title":"The incidence and clinical analysis of non-melanoma skin cancer","volume":"11","author":"Ci\u0105\u017cy\u0144ska","year":"2021","journal-title":"Sci Rep."},{"issue":"6","key":"10.1016\/j.jpi.2022.100159_bb0010","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0099674","article-title":"Non melanoma skin cancer and subsequent cancer risk","volume":"9","author":"Rees","year":"2014","journal-title":"PLoS One."},{"issue":"2","key":"10.1016\/j.jpi.2022.100159_bb0015","first-page":"357","article-title":"Early detection and treatment of skin cancer","volume":"62","author":"Jerant","year":"2000","journal-title":"AFP."},{"issue":"12","key":"10.1016\/j.jpi.2022.100159_bb0020","doi-asserted-by":"crossref","first-page":"1749","DOI":"10.1001\/jamaoncol.2019.2996","article-title":"Global, regional, and national cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life-years for 29 cancer groups, 1990 to 2017: a systematic analysis for the global burden of disease study","volume":"5","author":"Fitzmaurice","year":"2019","journal-title":"JAMA Oncol."},{"issue":"4","key":"10.1016\/j.jpi.2022.100159_bb0025","first-page":"893","article-title":"Prediction of skin cancer occurrence by ultraviolet solar index","volume":"3","author":"Rivas","year":"2012","journal-title":"Oncol Lett."},{"issue":"17","key":"10.1016\/j.jpi.2022.100159_bb0030","doi-asserted-by":"crossref","first-page":"1341","DOI":"10.1056\/NEJM199904293401707","article-title":"The pathogenesis of melanoma induced by ultraviolet radiation","volume":"340","author":"Gilchrest","year":"1999","journal-title":"New Engl J Med."},{"issue":"1","key":"10.1016\/j.jpi.2022.100159_bb0035","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1007\/s13671-014-0069-y","article-title":"Advanced basal cell carcinoma: epidemiology and therapeutic innovations","volume":"3","author":"Mohan","year":"2014","journal-title":"Curr Derm Rep."},{"issue":"15_suppl","key":"10.1016\/j.jpi.2022.100159_bb0040","doi-asserted-by":"crossref","first-page":"9058","DOI":"10.1200\/jco.2015.33.15_suppl.9058","article-title":"Trends in demographics, incidence, and survival in children, adolescents and young adults (AYA) with melanoma: A Surveillance, Epidemiology and End Results (SEER) population-based analysis","volume":"33","author":"Mitsis","year":"2015","journal-title":"JCO."},{"key":"10.1016\/j.jpi.2022.100159_bb0045","author":"Melanoma Skin Cancer Statistics"},{"key":"10.1016\/j.jpi.2022.100159_bb0050","author":"Melanoma - Statistics"},{"issue":"3","key":"10.1016\/j.jpi.2022.100159_bb0055","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.soncn.2013.06.002","article-title":"Skin cancer: an overview of epidemiology and risk factors","volume":"29","author":"Gordon","year":"2013","journal-title":"Semin Oncol Nurs."},{"issue":"9","key":"10.1016\/j.jpi.2022.100159_bb0060","doi-asserted-by":"crossref","first-page":"978","DOI":"10.1111\/j.1365-4632.2010.04474.x","article-title":"Review: ultraviolet radiation and skin cancer","volume":"49","author":"Narayanan","year":"2010","journal-title":"Int J Dermatol."},{"key":"10.1016\/j.jpi.2022.100159_bb0065","unstructured":"Cancer Facts & Figures 2022. Published online 2022:80."},{"issue":"4","key":"10.1016\/j.jpi.2022.100159_bb0070","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1016\/S0889-8588(05)70030-7","article-title":"Public health interventions for melanoma: prevention, early detection, and education","volume":"12","author":"Koh","year":"1998","journal-title":"Hematol\/Oncol Clin North Am."},{"issue":"1","key":"10.1016\/j.jpi.2022.100159_bb0075","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.jaad.2017.08.039","article-title":"Determination of the impact of melanoma surgical timing on survival using the National Cancer Database","volume":"78","author":"Conic","year":"2018","journal-title":"J Am Acad Dermatol."},{"issue":"1","key":"10.1016\/j.jpi.2022.100159_bb0080","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.juro.2010.03.021","article-title":"The value of mandatory second opinion pathology review of prostate needle biopsy interpretation before radical prostatectomy","volume":"184","author":"Brimo","year":"2010","journal-title":"J Urol."},{"issue":"11","key":"10.1016\/j.jpi.2022.100159_bb0085","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1001\/jama.2015.1405","article-title":"Diagnostic concordance among pathologists interpreting breast biopsy specimens","volume":"313","author":"Elmore","year":"2015","journal-title":"JAMA."},{"issue":"5","key":"10.1016\/j.jpi.2022.100159_bb0090","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1136\/gutjnl-2019-318985","article-title":"Histopathologist features predictive of diagnostic concordance at expert level among a large international sample of pathologists diagnosing Barrett\u2019s dysplasia using digital pathology","volume":"69","author":"van der Wel","year":"2020","journal-title":"Gut."},{"issue":"2","key":"10.1016\/j.jpi.2022.100159_bb0095","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":"7","key":"10.1016\/j.jpi.2022.100159_bb0100","doi-asserted-by":"crossref","first-page":"938","DOI":"10.1016\/S1470-2045(19)30333-X","article-title":"Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study","volume":"20","author":"Tschandl","year":"2019","journal-title":"Lancet Oncol."},{"key":"10.1016\/j.jpi.2022.100159_bb0105","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.ejca.2019.04.021","article-title":"Pathologist-level classification of histopathological melanoma images with deep neural networks","volume":"115","author":"Hekler","year":"2019","journal-title":"Eur J Cancer."},{"issue":"7","key":"10.1016\/j.jpi.2022.100159_bb0110","doi-asserted-by":"crossref","first-page":"934","DOI":"10.1097\/PAS.0000000000000198","article-title":"Histomorphologic assessment and interobserver diagnostic reproducibility of atypical spitzoid melanocytic neoplasms with long-term follow-up","volume":"38","author":"Gerami","year":"2014","journal-title":"Am J Surg Pathol."},{"key":"10.1016\/j.jpi.2022.100159_bb0115","article-title":"Pathologists\u2019 diagnosis of invasive melanoma and melanocytic proliferations: observer accuracy and reproducibility study","volume":"357","author":"Elmore","year":"2017","journal-title":"BMJ."},{"issue":"5","key":"10.1016\/j.jpi.2022.100159_bb0120","doi-asserted-by":"crossref","first-page":"751","DOI":"10.1016\/j.jaad.2009.09.043","article-title":"Discordance in the histopathologic diagnosis of melanoma at a melanoma referral center","volume":"62","author":"Shoo","year":"2010","journal-title":"J Am Acad Dermatol."},{"issue":"10","key":"10.1016\/j.jpi.2022.100159_bb0125","first-page":"1158","article-title":"Diagnosis of nonmelanoma skin cancer\/keratinocyte carcinoma: a review of diagnostic accuracy of nonmelanoma skin cancer diagnostic tests and technologies","volume":"33","author":"Mogensen","year":"2007","journal-title":"Dermatol Surg."},{"key":"10.1016\/j.jpi.2022.100159_bb0130","first-page":"10","article-title":"Superhistopath: a deep learning pipeline for mapping tumor heterogeneity on low-resolution whole-slide digital histopathology images","author":"Zormpas-Petridis","year":"2021","journal-title":"Front Oncol."},{"issue":"1","key":"10.1016\/j.jpi.2022.100159_bb0135","first-page":"51","article-title":"Agreement of dermatopathologists in the evaluation of clinically difficult melanocytic lesions: how golden is the \u2018gold standard\u2019?","volume":"224","author":"Braun","year":"2012","journal-title":"DRM."},{"issue":"7639","key":"10.1016\/j.jpi.2022.100159_bb0140","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"542","author":"Esteva","year":"2017","journal-title":"Nature."},{"issue":"12","key":"10.1016\/j.jpi.2022.100159_bb0145","doi-asserted-by":"crossref","first-page":"1366","DOI":"10.1111\/j.1365-4632.2009.04185.x","article-title":"Agreement between histological diagnosis of skin lesions by histopathologists and a dermato-histopathologist","volume":"48","author":"Heal","year":"2009","journal-title":"Int J Dermatol."},{"issue":"10","key":"10.1016\/j.jpi.2022.100159_bb0150","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1111\/cup.12566","article-title":"Utilizing the frequency of Benign, Atypical and Malignant diagnoses for quality improvement in the histopathologic diagnosis of melanocytic neoplasms","volume":"42","author":"Bush","year":"2015","journal-title":"J Cutan Pathol."},{"issue":"1","key":"10.1016\/j.jpi.2022.100159_bb0155","doi-asserted-by":"crossref","first-page":"26286","DOI":"10.1038\/srep26286","article-title":"Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis","volume":"6","author":"Litjens","year":"2016","journal-title":"Sci Rep."},{"issue":"1","key":"10.1016\/j.jpi.2022.100159_bb0160","doi-asserted-by":"crossref","first-page":"5","DOI":"10.7243\/2055-091X-1-5","article-title":"Past, present and future: overview on histology and histopathology","volume":"1","author":"Musumeci","year":"2014","journal-title":"J Histol Histopathol."},{"issue":"5","key":"10.1016\/j.jpi.2022.100159_bb0165","doi-asserted-by":"crossref","first-page":"e253","DOI":"10.1016\/S1470-2045(19)30154-8","article-title":"Digital pathology and artificial intelligence","volume":"20","author":"Niazi","year":"2019","journal-title":"Lancet Oncol."},{"key":"10.1016\/j.jpi.2022.100159_bb0170","doi-asserted-by":"crossref","first-page":"38","DOI":"10.4103\/jpi.jpi_53_18","article-title":"Artificial intelligence and digital pathology: challenges and opportunities","volume":"9","author":"Tizhoosh","year":"2018","journal-title":"J Pathol Inform."},{"issue":"2","key":"10.1016\/j.jpi.2022.100159_bb0175","first-page":"127","article-title":"Artificial intelligence in cancer imaging: clinical challenges and applications","volume":"69","author":"Bi","year":"2019","journal-title":"CA Cancer J Clin."},{"issue":"1","key":"10.1016\/j.jpi.2022.100159_bb0180","doi-asserted-by":"crossref","first-page":"9","DOI":"10.4103\/jpi.jpi_82_18","article-title":"Introduction to digital image analysis in whole-slide imaging: a white paper from the digital pathology association","volume":"10","author":"Aeffner","year":"2019","journal-title":"J Pathol Inform."},{"issue":"5","key":"10.1016\/j.jpi.2022.100159_bb0185","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 Intel."},{"issue":"1","key":"10.1016\/j.jpi.2022.100159_bb0190","doi-asserted-by":"crossref","first-page":"6311","DOI":"10.1038\/s41467-021-26643-8","article-title":"Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images","volume":"12","author":"Yu","year":"2021","journal-title":"Nat Commun."},{"issue":"10","key":"10.1016\/j.jpi.2022.100159_bb0195","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":"6","key":"10.1016\/j.jpi.2022.100159_bb0200","doi-asserted-by":"crossref","first-page":"866","DOI":"10.1111\/his.13305","article-title":"Application of automated image analysis reduces the workload of manual screening of sentinel lymph node biopsies in breast cancer","volume":"71","author":"Holten-Rossing","year":"2017","journal-title":"Histopathology."},{"key":"10.1016\/j.jpi.2022.100159_bb0205","unstructured":"Wang D, Khosla A, Gargeya R, Irshad H, Beck AH. Deep learning for identifying metastatic breast cancer. arXiv:1606.05718 [cs, q-bio]. Published online June 18, 2016. Accessed April 14, 2022."},{"issue":"4","key":"10.1016\/j.jpi.2022.100159_bb0210","doi-asserted-by":"crossref","first-page":"224","DOI":"10.3390\/jpm10040224","article-title":"The application of deep convolutional neural networks to brain cancer images: a survey","volume":"10","author":"Zadeh Shirazi","year":"2020","journal-title":"J Personal Med."},{"issue":"1","key":"10.1016\/j.jpi.2022.100159_bb0215","doi-asserted-by":"crossref","first-page":"4172","DOI":"10.1038\/s41598-017-04075-z","article-title":"Breast cancer multi-classification from histopathological images with structured deep learning model","volume":"7","author":"Han","year":"2017","journal-title":"Sci Rep."},{"key":"10.1016\/j.jpi.2022.100159_bb0220","first-page":"629","article-title":"A pathology deep learning system capable of triage of melanoma specimens utilizing dermatopathologist consensus as ground truth","author":"Sankarapandian","year":"2021","journal-title":"In"},{"issue":"1","key":"10.1016\/j.jpi.2022.100159_bb0225","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1001\/jamadermatol.2018.4378","article-title":"Expert-level diagnosis of nonpigmented skin cancer by combined convolutional neural networks","volume":"155","author":"Tschandl","year":"2019","journal-title":"JAMA Dermatol."},{"issue":"8","key":"10.1016\/j.jpi.2022.100159_bb0230","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."},{"key":"10.1016\/j.jpi.2022.100159_bb0235","first-page":"10","article-title":"Recognition of cutaneous melanoma on digitized histopathological slides via artificial intelligence algorithm","author":"De Logu","year":"2020","journal-title":"Front Oncol."},{"issue":"7","key":"10.1016\/j.jpi.2022.100159_bb0240","doi-asserted-by":"crossref","first-page":"1529","DOI":"10.1016\/j.jid.2018.01.028","article-title":"Classification of the clinical images for benign and malignant cutaneous tumors using a deep learning algorithm","volume":"138","author":"Han","year":"2018","journal-title":"J Investig Dermatol."},{"issue":"3","key":"10.1016\/j.jpi.2022.100159_bb0245","doi-asserted-by":"crossref","first-page":"754","DOI":"10.1111\/bjd.18026","article-title":"Recognizing basal cell carcinoma on smartphone-captured digital histopathology images with a deep neural network","volume":"182","author":"Jiang","year":"2020","journal-title":"Brit J Dermatol."},{"key":"10.1016\/j.jpi.2022.100159_bb0250","series-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2013","first-page":"403","article-title":"A deep learning architecture for image representation, visual interpretability and automated basal-cell carcinoma cancer detection","author":"Cruz-Roa","year":"2013"},{"key":"10.1016\/j.jpi.2022.100159_bb0255","series-title":"2013 International Conference on Circuits, Controls and Communications (CCUBE)","first-page":"1","article-title":"Melanoma skin cancer detection and classification based on supervised and unsupervised learning","author":"Mhaske","year":"2013"},{"issue":"1","key":"10.1016\/j.jpi.2022.100159_bb0260","doi-asserted-by":"crossref","first-page":"3217","DOI":"10.1038\/s41598-020-59985-2","article-title":"Tailored for real-world: a whole slide image classification system validated on uncurated multi-site data emulating the prospective pathology workload","volume":"10","author":"Ianni","year":"2020","journal-title":"Sci Rep."},{"issue":"9","key":"10.1016\/j.jpi.2022.100159_bb0265","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 Cybernet."},{"key":"10.1016\/j.jpi.2022.100159_bb0270","doi-asserted-by":"crossref","DOI":"10.1177\/2374289519873088","article-title":"Artificial intelligence and machine learning in pathology: the present landscape of supervised methods","volume":"6","author":"Rashidi","year":"2019","journal-title":"Acad Pathol."},{"key":"10.1016\/j.jpi.2022.100159_bb0275","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.jpi.2022.100159_bb0280","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1016\/j.neuroimage.2018.05.077","article-title":"A supervised learning approach for diffusion MRI quality control with minimal training data","volume":"178","author":"Graham","year":"2018","journal-title":"NeuroImage."},{"key":"10.1016\/j.jpi.2022.100159_bb0285","series-title":"Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis","first-page":"45","article-title":"Generation of a HER2 breast cancer gold-standard using supervised learning from multiple experts","author":"Chang","year":"2018"},{"key":"10.1016\/j.jpi.2022.100159_bb0290","series-title":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","first-page":"113","article-title":"Efficient machine learning framework for computer-aided detection of cerebral microbleeds using the Radon transform","author":"Fazlollahi","year":"2014"},{"issue":"1","key":"10.1016\/j.jpi.2022.100159_bb0295","doi-asserted-by":"crossref","first-page":"32","DOI":"10.4103\/jpi.jpi_31_18","article-title":"Diagnostic performance of deep learning algorithms applied to three common diagnoses in dermatopathology","volume":"9","author":"Olsen","year":"2018","journal-title":"J Pathol Inform."},{"key":"10.1016\/j.jpi.2022.100159_bb0300","series-title":"Computer Vision \u2013 ACCV 2012","first-page":"330","article-title":"Automatic skin lesion segmentation based on texture analysis and supervised learning","author":"He","year":"2013"},{"key":"10.1016\/j.jpi.2022.100159_bb0305","series-title":"2013 Seventh International Conference on Image and Graphics","first-page":"164","article-title":"Automatic skin lesion segmentation based on supervised learning","author":"Wu","year":"2013"},{"issue":"6","key":"10.1016\/j.jpi.2022.100159_bb0310","doi-asserted-by":"crossref","first-page":"6536","DOI":"10.3934\/mbe.2019326","article-title":"Convolutional neural networks for computer-aided detection or diagnosis in medical image analysis: an overview","volume":"16","author":"Gao","year":"2019","journal-title":"MBE."},{"issue":"3","key":"10.1016\/j.jpi.2022.100159_bb0315","first-page":"705","article-title":"The histogenesis and biologic behavior of primary human malignant melanomas of the skin","volume":"29","author":"Clark","year":"1969","journal-title":"Cancer Res."},{"issue":"1","key":"10.1016\/j.jpi.2022.100159_bb0320","doi-asserted-by":"crossref","first-page":"16878","DOI":"10.1038\/s41598-017-17204-5","article-title":"QuPath: open source software for digital pathology image analysis","volume":"7","author":"Bankhead","year":"2017","journal-title":"Sci Rep."},{"key":"10.1016\/j.jpi.2022.100159_bb0325","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"11976","article-title":"A ConvNet for the 2020s","author":"Liu","year":"2022"},{"key":"10.1016\/j.jpi.2022.100159_bb0330","first-page":"265","article-title":"Tensorflow: a system for large-scale machine learning","author":"Abadi","year":"2016","journal-title":"In"},{"key":"10.1016\/j.jpi.2022.100159_bb0335","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"7132","article-title":"Squeeze-and-excitation networks","author":"Hu","year":"2018"},{"key":"10.1016\/j.jpi.2022.100159_bb0340","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"10.1016\/j.jpi.2022.100159_bb0345","series-title":"European Conference on Computer Vision","first-page":"630","article-title":"Identity mappings in deep residual networks","author":"He","year":"2016"},{"key":"10.1016\/j.jpi.2022.100159_bb0350","series-title":"2009 IEEE Conference on Computer Vision and Pattern Recognition","first-page":"248","article-title":"ImageNet: a large-scale hierarchical image database","author":"Deng","year":"2009"},{"key":"10.1016\/j.jpi.2022.100159_bb0355","series-title":"Advances in Neural Information Processing Systems","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012"},{"key":"10.1016\/j.jpi.2022.100159_bb0360","series-title":"2015 IEEE International Conference on Computer Vision (ICCV)","first-page":"1026","article-title":"Delving deep into rectifiers: surpassing human-level performance on imagenet classification","author":"He","year":"2015"},{"issue":"1","key":"10.1016\/j.jpi.2022.100159_bb0365","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/s10479-005-5724-z","article-title":"A tutorial on the cross-entropy method","volume":"134","author":"de Boer","year":"2005","journal-title":"Ann Oper Res."},{"key":"10.1016\/j.jpi.2022.100159_bb0370","series-title":"2018 IEEE\/ACM 26th International Symposium on Quality of Service (IWQoS)","first-page":"1","article-title":"Improved adam optimizer for deep neural networks","author":"Zhang","year":"2018"},{"key":"10.1016\/j.jpi.2022.100159_bb0375","unstructured":"Loshchilov I, Hutter F. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101. Published online 2017."},{"key":"10.1016\/j.jpi.2022.100159_bb0380","unstructured":"Inoue H. Data augmentation by pairing samples for images classification. Published online April 11, 2018. arxiv:1801.02929."},{"key":"10.1016\/j.jpi.2022.100159_bb0385","author":"Lin"},{"key":"10.1016\/j.jpi.2022.100159_bb0390","series-title":"Proceedings of the 35th International Conference on Machine Learning","first-page":"2127","article-title":"Attention-based deep multiple instance learning","author":"Ilse","year":"2018"},{"key":"10.1016\/j.jpi.2022.100159_bb0395","unstructured":"Ridnik T, Ben-Baruch E, Noy A, Zelnik-Manor L. Imagenet-21k pretraining for the masses. Published online August 5, 2021. arxiv:2104.10972."},{"key":"10.1016\/j.jpi.2022.100159_bb0400","author":"React \u2013 A JavaScript library for building user interfaces"},{"issue":"1","key":"10.1016\/j.jpi.2022.100159_bb0405","first-page":"18","article-title":"Risk and survival of patients with head and neck cutaneous melanoma: national perspective","volume":"93","author":"Al-Qurayshi","year":"2017","journal-title":"OCL."},{"issue":"3","key":"10.1016\/j.jpi.2022.100159_bb0410","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1007\/s13671-017-0192-7","article-title":"Global burden of skin disease: inequities and innovations","volume":"6","author":"Seth","year":"2017","journal-title":"Curr Derm Rep."},{"issue":"1","key":"10.1016\/j.jpi.2022.100159_bb0415","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.jaad.2018.02.070","article-title":"Pathologist characteristics associated with accuracy and reproducibility of melanocytic skin lesion interpretation","volume":"79","author":"Elder","year":"2018","journal-title":"J Am Acad Dermatol."},{"issue":"2","key":"10.1016\/j.jpi.2022.100159_bb0420","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1111\/cup.14126","article-title":"Characterization of multiple diagnostic terms in melanocytic skin lesion pathology reports","volume":"49","author":"Chang","year":"2022","journal-title":"J Cutan Pathol."},{"issue":"10","key":"10.1016\/j.jpi.2022.100159_bb0425","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."},{"issue":"5","key":"10.1016\/j.jpi.2022.100159_bb0430","doi-asserted-by":"crossref","first-page":"784","DOI":"10.1111\/his.13691","article-title":"Evaluating the benefits of digital pathology implementation: time savings in laboratory logistics","volume":"73","author":"Baidoshvili","year":"2018","journal-title":"Histopathology."},{"issue":"6","key":"10.1016\/j.jpi.2022.100159_bb0435","doi-asserted-by":"crossref","first-page":"1544","DOI":"10.1016\/j.jaad.2020.01.028","article-title":"Characterizing the role of dermatologists in developing artificial intelligence for assessment of skin cancer","volume":"85","author":"Zakhem","year":"2021","journal-title":"J Am Acad Dermatol."}],"container-title":["Journal of Pathology Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2153353922007532?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2153353922007532?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T00:47:55Z","timestamp":1761871675000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2153353922007532"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":87,"alternative-id":["S2153353922007532"],"URL":"https:\/\/doi.org\/10.1016\/j.jpi.2022.100159","relation":{},"ISSN":["2153-3539"],"issn-type":[{"value":"2153-3539","type":"print"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"High-fidelity detection, subtyping, and localization of five skin neoplasms using supervised and semi-supervised learning","name":"articletitle","label":"Article Title"},{"value":"Journal of Pathology Informatics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jpi.2022.100159","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 The Author(s). Published by Elsevier Inc. on behalf of Association for Pathology Informatics.","name":"copyright","label":"Copyright"}],"article-number":"100159"}}