  Computer-aided pathology diagnosis based on the classification of Whole Slide
Image (WSI) plays an important role in clinical practice, and it is often
formulated as a weakly-supervised Multiple Instance Learning (MIL) problem.
Existing methods solve this problem from either a bag classification or an
instance classification perspective. In this paper, we propose an end-to-end
weakly supervised knowledge distillation framework (WENO) for WSI
classification, which integrates a bag classifier and an instance classifier in
a knowledge distillation framework to mutually improve the performance of both
classifiers. Specifically, an attention-based bag classifier is used as the
teacher network, which is trained with weak bag labels, and an instance
classifier is used as the student network, which is trained using the
normalized attention scores obtained from the teacher network as soft pseudo
labels for the instances in positive bags. An instance feature extractor is
shared between the teacher and the student to further enhance the knowledge
exchange between them. In addition, we propose a hard positive instance mining
strategy based on the output of the student network to force the teacher
network to keep mining hard positive instances. WENO is a plug-and-play
framework that can be easily applied to any existing attention-based bag
classification methods. Extensive experiments on five datasets demonstrate the
efficiency of WENO. Code is available at https://github.com/miccaiif/WENO.
