MED-TEX: Transferring and Explaining Knowledge with Less Data from Pretrained Medical Imaging Models. (arXiv:2008.02593v2 [cs.CV] UPDATED)

Deep learning methods usually require a large amount of training data and
lack interpretability. In this paper, we propose a novel knowledge distillation
and model interpretation framework for medical image classification that
jointly solves the above two issues. Specifically, to address the data-hungry
issue, a small student model is learned with less data by distilling knowledge
from a cumbersome pretrained teacher model. To interpret the teacher model and
assist the learning of the student, an explainer module is introduced to …

arxiv cv data medical imaging