Understanding the progression of cancer is at the core of cancer research. In this thesis we combine high resolution features with low resolution contextual features to automatic segment cancerous associated tissue in gigapixel histopathology whole slide images (WSIs) stained with hematoxylin and eosin. We take advantage of the multi-resolution data structure of WSIs and obtain contextual features through the use of dilated convolutions. Our proposed multi-resolution method has a comparable F1-score performance compared to a single low resolution method. Furthermore, the proposed method increases the F1-score by 34% for ductal Carcinoma In Situ (DCIS) and 12% for invasive ductal carcinoma (IDC) in comparison with a single high resolution method.
Lymphocytes play an important role in the progression of cancer. In the second part of this thesis, we boost the potential of the You Only Look Once (YOLO) architecture applied to automatic detection of lymphocytes in WSIs stained with immunohistochemistry by (1) tailoring the YOLO architecture to lymphocyte detection in WSI; (2) guiding training data sampling by exploiting prior knowledge on hard negative samples; (3) pairing the proposed sampling strategy with the focal loss technique. The combination of the proposed improvements increases the F1-score of YOLO by 3% with a speed-up of 4.3X.