Contrastive Multiple Instance Learning: An Unsupervised Framework for Learning Slide-Level Representations of Whole Slide Histopathology Images without Labels by Thomas E. Tavolara, Metin N. Gurcan, M. Khalid Khan Niazi

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Deep learning predicts gene expression as an intermediate data modality to identify susceptibility patterns in Mycobacterium tuberculosis infected Diversity Outbred mice by Thomas E. Tavolara, M.K.K. Niazi, Adam C. Gower, Melanie Ginese, Gillian Beamer, Metin N. Gurcan

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Semantic segmentation to identify bladder layers from H&E Images by Muhammad Khalid Khan Niazi, Enes Yazgan, Thomas E. Tavolara, Wencheng Li, Cheryl T. Lee, Anil Parwani, Metin N. Gurcan

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BCR-Net: A deep learning framework to predict breast cancer recurrence from histopathology images. by Ziyu Su, Muhammad Khalid Khan Niazi, Thomas E Tavolara, Shuo Niu, Gary H Tozbikian, Robert Wesolowski, Metin N Gurcan

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Translating prognostic quantification of c-MYC and BCL2 from tissue microarrays to whole slide images in diffuse large B-cell lymphoma using deep learning by Thomas E. Tavolara, M. Khalid Khan Niazi, Andrew L. Feldman, David L. Jaye, Christopher Flowers, Lee A.D. Cooper, Metin N. Gurcan

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