Enhancing Interpretability of Vertebrae Fracture Grading using Human-interpretable Prototypes
<|reference_start|>Enhancing Interpretability of Vertebrae Fracture Grading using Human-interpretable Prototypes: 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.<|reference_end|>
@article{sinhamahapatra2024enhancing,
    title={Enhancing Interpretability of Vertebrae Fracture Grading using
  Human-interpretable Prototypes},
    author={Poulami Sinhamahapatra, Suprosanna Shit, Anjany Sekuboyina, Malek
  Husseini, David Schinz, Nicolas Lenhart, Joern Menze, Jan Kirschke, Karsten
  Roscher, Stephan Guennemann},
    journal={Machine.Learning.for.Biomedical.Imaging. 2 (2024)},
    year={2024},
    doi={10.59275/j.melba.2024-258b},
    archivePrefix={arXiv},
    eprint={2404.02830},
    primaryClass={cs.CV cs.AI}
}