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 of vertebral fractures (prototypes) that reliably explain the model's decision in a human-understandable way. Specifically, we introduce a novel diversity-promoting loss to mitigate prototype repetitions in small datasets with intricate semantics. We have experimented with the VerSe'19 dataset and outperformed the existing prototype-based method. Further, our model provides superior interpretability against the post-hoc method. Importantly, expert radiologists validated the visual interpretability of our results, showing clinical applicability.
@article{arxiv.2404.02830,
title = {Enhancing Interpretability of Vertebrae Fracture Grading using Human-interpretable Prototypes},
author = {Poulami Sinhamahapatra and Suprosanna Shit and Anjany Sekuboyina and Malek Husseini and David Schinz and Nicolas Lenhart and Joern Menze and Jan Kirschke and Karsten Roscher and Stephan Guennemann},
journal= {arXiv preprint arXiv:2404.02830},
year = {2024}
}
Comments
Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org/2024:015