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

Whole-slide-imaging Cancer Metastases Detection and Localization with Limited Tumorous Data