Enhancing Interpretability of Vertebrae Fracture Grading using Human-interpretable Prototypes

arXiv:2404.02830v1 Announce Type: new
Abstract: Vertebral fracture grading classifies the severity of vertebral fractures, which is a challenging task in medical imaging and has recently attracted Deep Learning (DL) models. Only a few works attempted to make such models human-interpretable despite the need for transparency and trustworthiness in critical use cases like DL-assisted medical diagnosis. Moreover, such models either rely on post-hoc methods or additional annotations. In this work, we propose a novel interpretable-by-design method, ProtoVerse, to find relevant sub-parts …

abstract arxiv cases cs.ai cs.cv deep learning human imaging interpretability medical medical imaging transparency type use cases