Enhancing Interpretability of Vertebrae Fracture Grading using Human-interpretable Prototypes
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910547731021824 |
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| author | Sinhamahapatra, Poulami Shit, Suprosanna Sekuboyina, Anjany Husseini, Malek Schinz, David Lenhart, Nicolas Menze, Joern Kirschke, Jan Roscher, Karsten Guennemann, Stephan |
| author_facet | Sinhamahapatra, Poulami Shit, Suprosanna Sekuboyina, Anjany Husseini, Malek Schinz, David Lenhart, Nicolas Menze, Joern Kirschke, Jan Roscher, Karsten Guennemann, Stephan |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2404_02830 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Interpretability of Vertebrae Fracture Grading using Human-interpretable Prototypes Sinhamahapatra, Poulami Shit, Suprosanna Sekuboyina, Anjany Husseini, Malek Schinz, David Lenhart, Nicolas Menze, Joern Kirschke, Jan Roscher, Karsten Guennemann, Stephan Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | Enhancing Interpretability of Vertebrae Fracture Grading using Human-interpretable Prototypes |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2404.02830 |