Towards to reasonable decision basis in automatic bone x-ray image classification: A weakly-supervised approach

Abstract

A weakly-supervised framework is proposed that cannot only make class inference but also provides reasonable decision basis in bone X-ray images. We implement it in three stages progressively: (1) design a classification network and use positive class activation map (PCAM) for attention location; (2) generate masks from attention maps and lead the model to make classification prediction from the activation areas; (3) label lesions in very few images and guide the model to learn simultaneously. We test the proposed method on a bone X-ray dataset. Results show that it achieves significant improvements in lesion location.