Enhancing Interpretability of Vertebrae Fracture Grading using
  Human-interpretable Prototypes
Poulami Sinhamahapatra Suprosanna Shit Anjany Sekuboyina Malek Husseini David Schinz Nicolas Lenhart Joern Menze Jan Kirschke Karsten Roscher Stephan Guennemann
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.