Open Set Medical Diagnosis via Difficulty-Aware Multi-Label Thorax Disease Classification

Kyungdeuk Ko, Bokyeung Lee, Jonghwan Hong, Hanseok Ko

Abstract: Interest in emerging diseases is increasing due to recent global outbreaks like COVID-19. Unlike general image classification tasks, medical imaging is a multi-label classification that can have multiple diseases simultaneously. Then, suppose the results of all classes do not exceed the thresholds. In that case, it is classified as normal rather than unknown. This is why the existing open-set recognition (OSR) methods cannot be applied to open-set medical diagnosis. In this paper, we propose a novel open-set medical diagnosis method to solve the fundamental problem of OSR in multi-label classification. To solve this problem, we employ Copycat and entropy-based thresholds. To our knowledge, open-set multi-label medical diagnosis has not yet been addressed in research. We conduct experiments to confirm that our proposed method performs well in both multi-label classification and recognizing normal and unknown.

External IDs:dblp:conf/embc/KoLHK24