345 citations · 1.2k across the 23 of their papers we have counts for
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IMPaSh: A Novel Domain-shift Resistant Representation for Colorectal Cancer Tissue Classification
Trinh Thi Le Vuong, Quoc Dang Vu, Mostafa Jahanifar +3
The appearance of histopathology images depends on tissue type, staining and digitization procedure. These vary from source to source and are the potential causes for domain-shift…
Mitosis Detection, Fast and Slow: Robust and Efficient Detection of Mitotic Figures
Mostafa Jahanifar, Adam Shephard, Neda Zamanitajeddin +4
Counting of mitotic figures is a fundamental step in grading and prognostication of several cancers. However, manual mitosis counting is tedious and time-consuming. In addition, va…
TIAger: Tumor-Infiltrating Lymphocyte Scoring in Breast Cancer for the TiGER Challenge
Adam Shephard, Mostafa Jahanifar, Ruoyu Wang +6
The quantification of tumor-infiltrating lymphocytes (TILs) has been shown to be an independent predictor for prognosis of breast cancer patients. Typically, pathologists give an e…
One Model is All You Need: Multi-Task Learning Enables Simultaneous Histology Image Segmentation and Classification
Simon Graham, Quoc Dang Vu, Mostafa Jahanifar +4
The recent surge in performance for image analysis of digitised pathology slides can largely be attributed to the advances in deep learning. Deep models can be used to initially lo…