{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T07:25:20Z","timestamp":1772609120172,"version":"3.50.1"},"reference-count":77,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T00:00:00Z","timestamp":1732492800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000741","name":"University of Warwick","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000741","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100004325","name":"AstraZeneca PLC","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100004325","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.fr","clinicalkey.jp","clinicalkey.es","clinicalkey.com.au","clinicalkey.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computerized Medical Imaging and Graphics"],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1016\/j.compmedimag.2024.102466","type":"journal-article","created":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T10:16:11Z","timestamp":1732011371000},"page":"102466","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":8,"special_numbering":"C","title":["Dual attention model with reinforcement learning for classification of histology whole-slide images"],"prefix":"10.1016","volume":"118","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5136-8513","authenticated-orcid":false,"given":"Manahil","family":"Raza","sequence":"first","affiliation":[]},{"given":"Ruqayya","family":"Awan","sequence":"additional","affiliation":[]},{"given":"Raja Muhammad Saad","family":"Bashir","sequence":"additional","affiliation":[]},{"given":"Talha","family":"Qaiser","sequence":"additional","affiliation":[]},{"given":"Nasir M.","family":"Rajpoot","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.compmedimag.2024.102466_b1","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102473","article-title":"Self-rule to multi-adapt: Generalized multi-source feature learning using unsupervised domain adaptation for colorectal cancer tissue detection","volume":"79","author":"Abbet","year":"2022","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.compmedimag.2024.102466_b2","series-title":"The gumbel-max trick for discrete distributions","author":"Adams","year":"2013"},{"key":"10.1016\/j.compmedimag.2024.102466_b3","series-title":"Deep learning based prediction of MSI in colorectal cancer via prediction of the status of MMR markers","author":"Awan","year":"2022"},{"key":"10.1016\/j.compmedimag.2024.102466_b4","series-title":"Deep feature based cross-slide registration","author":"Awan","year":"2022"},{"key":"10.1016\/j.compmedimag.2024.102466_b5","article-title":"Comparative study between quantitative digital image analysis and fluorescence in situ hybridization of breast cancer equivocal human epidermal growth factor receptors 2 score 2+ cases","volume":"6","author":"Ayad","year":"2015","journal-title":"J. Pathol. Inf."},{"issue":"8","key":"10.1016\/j.compmedimag.2024.102466_b6","doi-asserted-by":"crossref","first-page":"1941","DOI":"10.1038\/s41591-023-02475-5","article-title":"A reinforcement learning model for AI-based decision support in skin cancer","volume":"29","author":"Barata","year":"2023","journal-title":"Nat. Med."},{"key":"10.1016\/j.compmedimag.2024.102466_b7","series-title":"Medical Imaging 2020: Digital Pathology","first-page":"245","article-title":"Automated grade classification of oral epithelial dysplasia using morphometric analysis of histology images","volume":"Vol. 11320","author":"Bashir","year":"2020"},{"key":"10.1016\/j.compmedimag.2024.102466_b8","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"129","article-title":"Predicting cancer with a recurrent visual attention model for histopathology images","author":"BenTaieb","year":"2018"},{"key":"10.1016\/j.compmedimag.2024.102466_b9","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1007\/s10549-011-1744-3","article-title":"Genetic heterogeneity in HER2 testing may influence therapy eligibility","volume":"133","author":"Bernasconi","year":"2012","journal-title":"Breast Cancer Res. Treat."},{"key":"10.1016\/j.compmedimag.2024.102466_b10","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2023.102885","article-title":"An aggregation of aggregation methods in computational pathology","author":"Bilal","year":"2023","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.compmedimag.2024.102466_b11","series-title":"Decoding the visual attention of pathologists to reveal their level of expertise","author":"Chakraborty","year":"2024"},{"key":"10.1016\/j.compmedimag.2024.102466_b12","article-title":"The evolving role of artificial intelligence in gastrointestinal histopathology: An update","author":"Codipilly","year":"2023","journal-title":"Clin. Gastroenterol. Hepatol."},{"key":"10.1016\/j.compmedimag.2024.102466_b13","series-title":"Cellular segmentation and composition in routine histology images using deep learning","author":"Dawood","year":"2022"},{"key":"10.1016\/j.compmedimag.2024.102466_b14","doi-asserted-by":"crossref","first-page":"BMI","DOI":"10.4137\/BMI.S2185","article-title":"Immunohistochemistry as an important tool in biomarkers detection and clinical practice","volume":"5","author":"De Matos","year":"2010","journal-title":"Biomark. Insights"},{"issue":"18","key":"10.1016\/j.compmedimag.2024.102466_b15","doi-asserted-by":"crossref","first-page":"2806","DOI":"10.1016\/j.ejca.2008.09.013","article-title":"Her2-positive breast cancer: herceptin and beyond","volume":"44","author":"Dean-Colomb","year":"2008","journal-title":"Eur. J. Cancer"},{"key":"10.1016\/j.compmedimag.2024.102466_b16","doi-asserted-by":"crossref","first-page":"264","DOI":"10.3389\/fmed.2019.00264","article-title":"Deep learning for whole slide image analysis: an overview","volume":"6","author":"Dimitriou","year":"2019","journal-title":"Front. Med."},{"key":"10.1016\/j.compmedimag.2024.102466_b17","series-title":"Whole-slide image preprocessing in python","author":"Eriksson","year":"2018"},{"key":"10.1016\/j.compmedimag.2024.102466_b18","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2024.102337","article-title":"Multiple instance learning for digital pathology: A review of the state-of-the-art, limitations & future potential","author":"Gadermayr","year":"2024","journal-title":"Comput. Med. Imaging Graph."},{"key":"10.1016\/j.compmedimag.2024.102466_b19","doi-asserted-by":"crossref","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":"10.1016\/j.compmedimag.2024.102466_b20","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J., 2016. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"10.1016\/j.compmedimag.2024.102466_b21","series-title":"2020 IEEE 17th International Symposium on Biomedical Imaging","first-page":"235","article-title":"Multiple instance learning via deep hierarchical exploration for histology image classification","author":"Hering","year":"2020"},{"key":"10.1016\/j.compmedimag.2024.102466_b22","series-title":"Towards Integrative Machine Learning and Knowledge Extraction","first-page":"1","article-title":"Towards integrative machine learning and knowledge extraction","author":"Holzinger","year":"2017"},{"key":"10.1016\/j.compmedimag.2024.102466_b23","series-title":"Computational pathology: A survey review and the way forward","author":"Hosseini","year":"2023"},{"issue":"2","key":"10.1016\/j.compmedimag.2024.102466_b24","doi-asserted-by":"crossref","DOI":"10.1002\/acm2.13898","article-title":"Reinforcement learning in med. image anal.: Concepts, applications, challenges, and future directions","volume":"24","author":"Hu","year":"2023","journal-title":"J. Appl. Clin. Med. Phys."},{"issue":"1","key":"10.1016\/j.compmedimag.2024.102466_b25","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1056\/NEJMra043186","article-title":"Trastuzumab\u2014mechanism of action and use in clinical practice","volume":"357","author":"Hudis","year":"2007","journal-title":"New Engl. J. Med."},{"key":"10.1016\/j.compmedimag.2024.102466_b26","series-title":"International Conference on Machine Learning","first-page":"2127","article-title":"Attention-based deep multiple instance learning","author":"Ilse","year":"2018"},{"key":"10.1016\/j.compmedimag.2024.102466_b27","series-title":"Domain generalization in computational pathology: survey and guidelines","author":"Jahanifar","year":"2023"},{"key":"10.1016\/j.compmedimag.2024.102466_b28","series-title":"Development and validation of fully automatic deep learning-based algorithms for immunohistochemistry reporting of invasive breast ductal carcinoma","author":"Jha","year":"2024"},{"key":"10.1016\/j.compmedimag.2024.102466_b29","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.122051","article-title":"The utility of a deep learning-based approach in Her-2\/neu assessment in breast cancer","volume":"238","author":"Kabir","year":"2024","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"10.1016\/j.compmedimag.2024.102466_b30","doi-asserted-by":"crossref","DOI":"10.1002\/ima.22976","article-title":"HMARNET\u2014A hierarchical multi-attention residual network for gleason scoring of prostate cancer","volume":"34","author":"Karthik","year":"2024","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"10.1016\/j.compmedimag.2024.102466_b31","series-title":"International Conference on Machine Learning","first-page":"3282","article-title":"Processing megapixel images with deep attention-sampling models","author":"Katharopoulos","year":"2019"},{"issue":"4","key":"10.1016\/j.compmedimag.2024.102466_b32","first-page":"331","article-title":"How does a pathologist make a diagnosis?","volume":"84","author":"King","year":"1967","journal-title":"Arch. Pathol."},{"key":"10.1016\/j.compmedimag.2024.102466_b33","series-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014"},{"key":"10.1016\/j.compmedimag.2024.102466_b34","doi-asserted-by":"crossref","unstructured":"Kong, F., Henao, R., 2022. Efficient Classification of Very Large Images with Tiny Objects. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 2384\u20132394.","DOI":"10.1109\/CVPR52688.2022.00242"},{"key":"10.1016\/j.compmedimag.2024.102466_b35","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.compmedimag.2024.102466_b36","series-title":"Lenet-5, convolutional neural networks","author":"LeCun","year":"2015"},{"key":"10.1016\/j.compmedimag.2024.102466_b37","article-title":"Mechanisms and functions of DNA mismatch repair","volume":"18","author":"Li","year":"2008","journal-title":"Cell Res."},{"key":"10.1016\/j.compmedimag.2024.102466_b38","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.120280","article-title":"DSCA: A dual-stream network with cross-attention on whole-slide image pyramids for cancer prognosis","volume":"227","author":"Liu","year":"2023","journal-title":"Expert Syst. Appl."},{"issue":"6","key":"10.1016\/j.compmedimag.2024.102466_b39","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":"10.1016\/j.compmedimag.2024.102466_b40","doi-asserted-by":"crossref","DOI":"10.1093\/annonc\/mdz116","article-title":"ESMO recommendations on microsatellite instability testing for immunotherapy in cancer, and its relationship with PD-1\/PD-L1 expression and tumour mutational burden: a systematic review-based approach","volume":"30","author":"Luchini","year":"2019","journal-title":"Ann. Oncol."},{"key":"10.1016\/j.compmedimag.2024.102466_b41","doi-asserted-by":"crossref","unstructured":"Maksoud, S., Zhao, K., Hobson, P., Jennings, A., Lovell, B.C., 2020. Sos: Selective objective switch for rapid immunofluorescence whole slide image classification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 3862\u20133871.","DOI":"10.1109\/CVPR42600.2020.00392"},{"key":"10.1016\/j.compmedimag.2024.102466_b42","series-title":"Annual Conference on Medical Image Understanding and Analysis","article-title":"A robust algorithm for automated HER2 scoring in breast cancer histology slides using characteristic curves","author":"Mukundan","year":"2017"},{"issue":"2","key":"10.1016\/j.compmedimag.2024.102466_b43","doi-asserted-by":"crossref","first-page":"35","DOI":"10.3390\/jimaging4020035","article-title":"Image features based on characteristic curves and local binary patterns for automated HER2 scoring","volume":"4","author":"Mukundan","year":"2018","journal-title":"J. Imaging"},{"issue":"3","key":"10.1016\/j.compmedimag.2024.102466_b44","doi-asserted-by":"crossref","first-page":"35","DOI":"10.3390\/jimaging5030035","article-title":"Analysis of image feature characteristics for automated scoring of HER2 in histology slides","volume":"5","author":"Mukundan","year":"2019","journal-title":"J. Imaging"},{"issue":"7","key":"10.1016\/j.compmedimag.2024.102466_b45","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1056\/NEJMoa1914609","article-title":"Tucatinib, trastuzumab, and capecitabine for HER2-positive metastatic breast cancer","volume":"382","author":"Murthy","year":"2020","journal-title":"New Engl. J. Med."},{"key":"10.1016\/j.compmedimag.2024.102466_b46","series-title":"Molecular testing strategies for lynch syndrome in people with colorectal cancer","author":"National Institute for Health and Care Excellence (NICE)","year":"2017"},{"key":"10.1016\/j.compmedimag.2024.102466_b47","series-title":"Immunohistochemistry: A Technical Guide to Current Practices","author":"Nguyen","year":"2022"},{"issue":"5","key":"10.1016\/j.compmedimag.2024.102466_b48","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."},{"issue":"5","key":"10.1016\/j.compmedimag.2024.102466_b49","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1080\/10520295.2016.1179342","article-title":"A standard tissue as a control for histochemical and immunohistochemical staining","volume":"91","author":"Otali","year":"2016","journal-title":"Biotech. Histochem."},{"issue":"1","key":"10.1016\/j.compmedimag.2024.102466_b50","doi-asserted-by":"crossref","first-page":"124","DOI":"10.5858\/133.1.124","article-title":"How does a pathologist make a diagnosis?","volume":"133","author":"Pena","year":"2009","journal-title":"Arch. Pathol. Lab. Med."},{"key":"10.1016\/j.compmedimag.2024.102466_b51","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2023.102261","article-title":"Interpretable HER2 scoring by evaluating clinical guidelines through a weakly supervised, constrained deep learning approach","volume":"108","author":"Pham","year":"2023","journal-title":"Comput. Med. Imaging Graph."},{"key":"10.1016\/j.compmedimag.2024.102466_b52","unstructured":"Pitk\u00e4aho, T., Lehtim\u00e4ki, T.M., McDonald, J., Naughton, T.J., et al., 2016. Classifying HER2 breast cancer cell samples using deep learning. In: Proc. Irish Mach. Vis. Image Process. Conf. pp. 1\u2013104."},{"issue":"1","key":"10.1016\/j.compmedimag.2024.102466_b53","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s43856-022-00186-5","article-title":"TIAToolbox as an end-to-end library for advanced tissue image analytics","volume":"2","author":"Pocock","year":"2022","journal-title":"Commun. Med."},{"issue":"2","key":"10.1016\/j.compmedimag.2024.102466_b54","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1111\/his.13333","article-title":"Her 2 challenge contest: a detailed assessment of automated her 2 scoring algorithms in whole slide images of breast cancer tissues","volume":"72","author":"Qaiser","year":"2018","journal-title":"Histopathology"},{"issue":"11","key":"10.1016\/j.compmedimag.2024.102466_b55","doi-asserted-by":"crossref","first-page":"2620","DOI":"10.1109\/TMI.2019.2907049","article-title":"Learning where to see: A novel attention model for automated immunohistochemical scoring","volume":"38","author":"Qaiser","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"4","key":"10.1016\/j.compmedimag.2024.102466_b56","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1136\/jcp-2022-208632","article-title":"UK recommendations for HER2 assessment in breast cancer: an update","volume":"76","author":"Rakha","year":"2023","journal-title":"J. Clin. Pathol."},{"key":"10.1016\/j.compmedimag.2024.102466_b57","unstructured":"Raza, M., Bashir, S., Qaiser, T., Analytics, N.R.-T.I., Raza, M., Bashir, S., Qaiser, T., Rajpoot, N., 2023. Stain-invariant representation for tissue classification in histology images. In: 27th Conference on Medical Image Understanding and Analysi 2023. p. 242."},{"issue":"2","key":"10.1016\/j.compmedimag.2024.102466_b58","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1515\/cdbme-2017-0171","article-title":"Deep bilinear features for Her2 scoring in digital pathology","volume":"3","author":"Rodner","year":"2017","journal-title":"Curr. Dir. Biomed. Eng."},{"issue":"5","key":"10.1016\/j.compmedimag.2024.102466_b59","doi-asserted-by":"crossref","first-page":"2189","DOI":"10.1109\/TIP.2018.2795742","article-title":"Her2Net: A deep framework for semantic segmentation and classification of cell membranes and nuclei in breast cancer evaluation","volume":"27","author":"Saha","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.compmedimag.2024.102466_b60","series-title":"Innovations in Computational Intelligence and Computer Vision","first-page":"319","article-title":"Dan: Breast cancer classification from high-resolution histology images using deep attention network","author":"Sanyal","year":"2021"},{"key":"10.1016\/j.compmedimag.2024.102466_b61","doi-asserted-by":"crossref","DOI":"10.34133\/bmef.0048","article-title":"Automated HER2 scoring in breast cancer images using deep learning and pyramid sampling","author":"Selcuk","year":"2024","journal-title":"BMEF (BME Front.)"},{"key":"10.1016\/j.compmedimag.2024.102466_b62","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A., 2015. Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1\u20139.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"10.1016\/j.compmedimag.2024.102466_b63","doi-asserted-by":"crossref","unstructured":"Tang, W., Huang, S., Zhang, X., Zhou, F., Zhang, Y., Liu, B., 2023. Multiple instance learning framework with masked hard instance mining for whole slide image classification. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 4078\u20134087.","DOI":"10.1109\/ICCV51070.2023.00377"},{"issue":"4","key":"10.1016\/j.compmedimag.2024.102466_b64","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1111\/j.1365-2559.2006.02513.x","article-title":"Quantification of immunohistochemistry\u2014issues concerning methods, utility and semiquantitative assessment II","volume":"49","author":"Taylor","year":"2006","journal-title":"Histopathology"},{"key":"10.1016\/j.compmedimag.2024.102466_b65","series-title":"Computer Vision\u2013ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23\u201327, 2022, Proceedings, Part XXI","first-page":"699","article-title":"Differentiable zooming for multiple instance learning on whole-slide images","author":"Thandiackal","year":"2022"},{"key":"10.1016\/j.compmedimag.2024.102466_b66","series-title":"Registration and multi-immunohistochemical analysis of whole slide images of serial tissue sections","author":"Trahearn","year":"2017"},{"key":"10.1016\/j.compmedimag.2024.102466_b67","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2020.101838","article-title":"An end-to-end breast tumour classification model using context-based patch modelling\u2013A BiLSTM approach for image classification","volume":"87","author":"Tripathi","year":"2021","journal-title":"Comput. Med. Imaging Graph."},{"key":"10.1016\/j.compmedimag.2024.102466_b68","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2023.102270","article-title":"Weakly supervised bilayer convolutional network in segmentation of HER2 related cells to guide HER2 targeted therapies","volume":"108","author":"Wang","year":"2023","journal-title":"Comput. Med. Imaging Graph."},{"issue":"2","key":"10.1016\/j.compmedimag.2024.102466_b69","first-page":"31","article-title":"Colorectal cancer: molecular features and clinical opportunities","volume":"31","author":"Worthley","year":"2010","journal-title":"Clin. Biochem. Rev."},{"key":"10.1016\/j.compmedimag.2024.102466_b70","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0233678","article-title":"Deep learning-based survival prediction for multiple cancer types using histopathology images","volume":"15","author":"Wulczyn","year":"2020","journal-title":"PLoS One"},{"issue":"6","key":"10.1016\/j.compmedimag.2024.102466_b71","doi-asserted-by":"crossref","first-page":"1930","DOI":"10.1109\/TMI.2019.2962013","article-title":"Attention by selection: A deep selective attention approach to breast cancer classification","volume":"39","author":"Xu","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"5","key":"10.1016\/j.compmedimag.2024.102466_b72","doi-asserted-by":"crossref","first-page":"1306","DOI":"10.1109\/TMI.2019.2948026","article-title":"Guided soft attention network for classification of breast cancer histopathology images","volume":"39","author":"Yang","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.compmedimag.2024.102466_b73","doi-asserted-by":"crossref","unstructured":"Zhang, J., Ma, K., Van Arnam, J., Gupta, R., Saltz, J., Vakalopoulou, M., Samaras, D., 2021. A Joint Spatial and Magnification Based Attention Framework for Large Scale Histopathology Classification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 3776\u20133784.","DOI":"10.1109\/CVPRW53098.2021.00418"},{"key":"10.1016\/j.compmedimag.2024.102466_b74","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2023.102275","article-title":"Learning how to detect: A deep reinforcement learning method for whole-slide melanoma histopathology images","volume":"108","author":"Zheng","year":"2023","journal-title":"Comput. Med. Imaging Graph."},{"key":"10.1016\/j.compmedimag.2024.102466_b75","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2021.101861","article-title":"Histopathology classification and localization of colorectal cancer using global labels by weakly supervised deep learning","volume":"88","author":"Zhou","year":"2021","journal-title":"Comput. Med. Imaging Graph."},{"key":"10.1016\/j.compmedimag.2024.102466_b76","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.102193","article-title":"Deep reinforcement learning in medical imaging: A literature review","volume":"73","author":"Zhou","year":"2021","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.compmedimag.2024.102466_b77","doi-asserted-by":"crossref","first-page":"90931","DOI":"10.1109\/ACCESS.2020.2993788","article-title":"A comprehensive review for breast histopathology image analysis using classical and deep neural networks","volume":"8","author":"Zhou","year":"2020","journal-title":"IEEE Access"}],"container-title":["Computerized Medical Imaging and Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0895611124001435?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0895611124001435?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,3,6]],"date-time":"2025-03-06T14:32:32Z","timestamp":1741271552000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0895611124001435"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12]]},"references-count":77,"alternative-id":["S0895611124001435"],"URL":"https:\/\/doi.org\/10.1016\/j.compmedimag.2024.102466","relation":{},"ISSN":["0895-6111"],"issn-type":[{"value":"0895-6111","type":"print"}],"subject":[],"published":{"date-parts":[[2024,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Dual attention model with reinforcement learning for classification of histology whole-slide images","name":"articletitle","label":"Article Title"},{"value":"Computerized Medical Imaging and Graphics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compmedimag.2024.102466","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 The Authors. Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"102466"}}