  Recently, various deep learning methods have shown significant successes in
medical image analysis, especially in the detection of cancer metastases in
hematoxylin and eosin (H&E) stained whole-slide images (WSIs). However, in
order to obtain good performance, these research achievements rely on hundreds
of well-annotated WSIs. In this study, we tackle the tumor localization and
detection problem under the setting of few labeled whole slide images and
introduce a patch-based analysis pipeline based on the latest reverse knowledge
distillation architecture. To address the extremely unbalanced normal and
tumorous samples in training sample collection, we applied the focal loss
formula to the representation similarity metric for model optimization.
Compared with prior arts, our method achieves similar performance by less than
ten percent of training samples on the public Camelyon16 dataset. In addition,
this is the first work that show the great potential of the knowledge
distillation models in computational histopathology.
