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In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2020, p. 3852\u201361.","DOI":"10.1109\/CVPR42600.2020.00391"},{"issue":"6","key":"10.1016\/j.compeleceng.2026.111204_b68","doi-asserted-by":"crossref","first-page":"1091","DOI":"10.1136\/amiajnl-2012-001469","article-title":"Cancer Digital Slide Archive: an informatics resource to support integrated in silico analysis of TCGA pathology data","volume":"20","author":"Gutman","year":"2013","journal-title":"J Am Med Informatics Assoc"},{"issue":"6","key":"10.1016\/j.compeleceng.2026.111204_b69","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":"10.1016\/j.compeleceng.2026.111204_b70","doi-asserted-by":"crossref","unstructured":"Zhang X, Zhou X, Lin M, Sun J. Shufflenet: An extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2018, p. 6848\u201356.","DOI":"10.1109\/CVPR.2018.00716"},{"key":"10.1016\/j.compeleceng.2026.111204_b71","series-title":"Smooth loss functions for deep top-k classification","author":"Berrada","year":"2018"},{"key":"10.1016\/j.compeleceng.2026.111204_b72","doi-asserted-by":"crossref","DOI":"10.1016\/j.jpi.2022.100104","article-title":"An analysis of pathologists\u2019 viewing processes as they diagnose whole slide digital images","volume":"13","author":"Ghezloo","year":"2022","journal-title":"J Pathol Inform."},{"issue":"2","key":"10.1016\/j.compeleceng.2026.111204_b73","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1111\/his.12629","article-title":"Slide navigation patterns among pathologists with long experience of digital review","volume":"67","author":"Molin","year":"2015","journal-title":"Histopathology"},{"key":"10.1016\/j.compeleceng.2026.111204_b74","series-title":"European conference on computer vision","first-page":"699","article-title":"Differentiable zooming for multiple instance learning on whole-slide images","author":"Thandiackal","year":"2022"},{"key":"10.1016\/j.compeleceng.2026.111204_b75","series-title":"MICCAI workshop on computational pathology","first-page":"170","article-title":"Multi-scale task multiple instance learning for the classification of digital pathology images with global annotations","author":"Marini","year":"2021"},{"key":"10.1016\/j.compeleceng.2026.111204_b76","doi-asserted-by":"crossref","unstructured":"Tang W, Huang S, Zhang X, Zhou F, Zhang Y, Liu B. 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. ICCV, 2023, p. 4078\u201387.","DOI":"10.1109\/ICCV51070.2023.00377"},{"key":"10.1016\/j.compeleceng.2026.111204_b77","series-title":"International conference on medical image computing and computer-assisted intervention","first-page":"594","article-title":"Structured state space models for multiple instance learning in digital pathology","author":"Fillioux","year":"2023"},{"issue":"1","key":"10.1016\/j.compeleceng.2026.111204_b78","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1109\/TPAMI.2022.3145392","article-title":"Deep roc analysis and auc as balanced average accuracy, for improved classifier selection, audit and explanation","volume":"45","author":"Carrington","year":"2022","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"1","key":"10.1016\/j.compeleceng.2026.111204_b79","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":"2017","journal-title":"IEEE Trans Med Imaging"},{"issue":"4","key":"10.1016\/j.compeleceng.2026.111204_b80","first-page":"251","article-title":"Explainability and causability in digital pathology","volume":"9","author":"Plass","year":"2023","journal-title":"J Pathol: Clin Res"},{"issue":"11","key":"10.1016\/j.compeleceng.2026.111204_b81","doi-asserted-by":"crossref","first-page":"3679","DOI":"10.1109\/TMI.2020.3002417","article-title":"Optimization for medical image segmentation: theory and practice when evaluating with dice score or jaccard index","volume":"39","author":"Eelbode","year":"2020","journal-title":"IEEE Trans Med Imaging"},{"key":"10.1016\/j.compeleceng.2026.111204_b82","article-title":"Comparing sets of patterns with the Jaccard index","volume":"22","author":"Fletcher","year":"2018","journal-title":"Australas J Inf Syst"},{"issue":"10","key":"10.1016\/j.compeleceng.2026.111204_b83","doi-asserted-by":"crossref","first-page":"1084","DOI":"10.1007\/s11263-017-1059-x","article-title":"Top-down neural attention by excitation backprop","volume":"126","author":"Zhang","year":"2018","journal-title":"Int J Comput Vis"},{"key":"10.1016\/j.compeleceng.2026.111204_b84","series-title":"2015 national aerospace and electronics conference","first-page":"27","article-title":"Using roc curves and auc to evaluate performance of no-reference image fusion metrics","author":"Ferris","year":"2015"},{"key":"10.1016\/j.compeleceng.2026.111204_b85","unstructured":"Hsu H, Lachenbruch PA. Paired t test. Wiley StatsRef: Stat Ref Online 2014."},{"issue":"1","key":"10.1016\/j.compeleceng.2026.111204_b86","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1111\/rssb.12298","article-title":"Multiple testing with the structure-adaptive benjamini\u2013hochberg algorithm","volume":"81","author":"Li","year":"2019","journal-title":"J R Stat Soc Ser B Stat Methodol"},{"issue":"3","key":"10.1016\/j.compeleceng.2026.111204_b87","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1038\/s41592-019-0686-2","article-title":"SciPy 1.0: fundamental algorithms for scientific computing in Python","volume":"17","author":"Virtanen","year":"2020","journal-title":"Nature Methods"},{"issue":"7","key":"10.1016\/j.compeleceng.2026.111204_b88","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1109\/TSE.2018.2794977","article-title":"The impact of automated parameter optimization on defect prediction models","volume":"45","author":"Tantithamthavorn","year":"2018","journal-title":"IEEE Trans Softw Eng"},{"issue":"1","key":"10.1016\/j.compeleceng.2026.111204_b89","first-page":"3","article-title":"ScottKnott: A Package for Performing the Scott-Knott Clustering Algorithm in R.","volume":"15","author":"Jelihovschi","year":"2014","journal-title":"Trends Appl Comput Math"},{"issue":"1","key":"10.1016\/j.compeleceng.2026.111204_b90","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.ccell.2022.12.001","article-title":"Histopathologic and proteogenomic heterogeneity reveals features of clear cell renal cell carcinoma aggressiveness","volume":"41","author":"Li","year":"2023","journal-title":"Cancer Cell"},{"issue":"11","key":"10.1016\/j.compeleceng.2026.111204_b91","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1145\/3458652","article-title":"Medical artificial intelligence: the european legal perspective","volume":"64","author":"K. 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