Enhancing Interpretability of Vertebrae Fracture Grading using Human-interpretable Prototypes

• URL: http://arxiv.org/abs/2404.02830v2
• Date: Wed, 31 Jul 2024 12:34:39 GMT
• Title: Enhancing Interpretability of Vertebrae Fracture Grading using Human-interpretable Prototypes
• Authors: Poulami Sinhamahapatra, Suprosanna Shit, Anjany Sekuboyina, Malek Husseini, David Schinz, Nicolas Lenhart, Joern Menze, Jan Kirschke, Karsten Roscher, Stephan Guennemann,
• Abstract summary: 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. We have experimented with the VerSe'19 dataset and outperformed the existing prototype-based method.
• Score: 7.633493982907541
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

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