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Multi-Instance Multi-Label Learning for Multi-Class Classification of Whole Slide Breast Histopathology Images., , , , , and . IEEE Trans. Med. Imaging, 37 (1): 316-325 (2018)Deep Feature Representations for Variable-Sized Regions of Interest in Breast Histopathology., , , , , , and . IEEE J. Biomed. Health Informatics, 25 (6): 2041-2049 (2021)Analysis of Regions of Interest and Distractor Regions in Breast Biopsy Images., , , , , and . BHI, page 1-4. IEEE, (2021)Pupil diameter changes reflect difficulty and diagnostic accuracy during medical image interpretation., , , , , and . BMC Medical Informatics Decis. Mak., (2016)Learning to Segment Breast Biopsy Whole Slide Images., , , , , and . CoRR, (2017)Characterizing Diagnostic Search Patterns in Digital Breast Pathology: Scanners and Drillers., , , , and . J. Digital Imaging, 31 (1): 32-41 (2018)Accuracy is in the eyes of the pathologist: The visual interpretive process and diagnostic accuracy with digital whole slide images., , , and . J. Biomed. Informatics, (2017)Y-Net: Joint Segmentation and Classification for Diagnosis of Breast Biopsy Images., , , , , and . MICCAI (2), volume 11071 of Lecture Notes in Computer Science, page 893-901. Springer, (2018)Localization of Diagnostically Relevant Regions of Interest in Whole Slide Images: a Comparative Study., , , , , and . J. Digital Imaging, 29 (4): 496-506 (2016)From patch-level to ROI-level deep feature representations for breast histopathology classification., , , , , and . Medical Imaging: Digital Pathology, volume 10956 of SPIE Proceedings, page 109560H. SPIE, (2019)