Knowledge Sharing via Mimicking Attention Guided-Discriminative Features in Whole Slide Image Classification

The difficulty of collecting histopathology whole slide images (WSIs) and lack of disease-positives within slide image is a major obstacle to the development of computer-aided diagnosis. Existing works suggest sharing knowledge learned by mimicking the discriminative features, in which a student model with lack-features is trained to mimic a teacher model with rich-features. However, most feature mimicking methods, designed for natural image tasks, might be failed in the case of whole slide images. We propose a new method to mimic features for knowledge sharing in WSI classification. On the one hand, attention guided feature selection and normalization is proposed to extract discriminative features from a learning model and use attention scores to quantify feature contributions so as to identify the diseases-positive regions (a.k.a Region of Interests). On the other hand, we propose to learn by mimicking the high-discriminative features based on disease-positive regions. Our method is evaluated on two datasets with rich-features and two datasets with lack-features. Results demonstrate that our proposed method can boost performance compared to competitive MIL and knowledge sharing methods at the WSI level.