Summary of Enhancing Interpretability Of Vertebrae Fracture Grading Using Human-interpretable Prototypes, by Poulami Sinhamahapatra et al.
Enhancing Interpretability of Vertebrae Fracture Grading using Human-interpretable Prototypes
by Poulami Sinhamahapatra, Suprosanna Shit, Anjany Sekuboyina, Malek Husseini, David Schinz, Nicolas Lenhart, Joern Menze, Jan Kirschke, Karsten Roscher, Stephan Guennemann
First submitted to arxiv on: 3 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel interpretable-by-design method called ProtoVerse to classify vertebral fracture severity in medical imaging using Deep Learning models. The method identifies relevant sub-parts (prototypes) that explain the model’s decision-making process in a human-understandable way. To mitigate prototype repetitions in small datasets with intricate semantics, the authors introduce a diversity-promoting loss function. Experimental results on the VerSe’19 dataset show that ProtoVerse outperforms existing prototype-based methods and provides superior interpretability compared to post-hoc methods. Importantly, expert radiologists validated the visual interpretability of the results, indicating clinical applicability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps doctors better understand why a medical image shows a certain type of vertebral fracture. They create a new way to make Deep Learning models more transparent and trustworthy by finding important parts (prototypes) that explain how the model made its decision. This is important for critical uses like diagnosing medical conditions. The authors test their method on a dataset and show it works better than other similar methods. Doctors even agree that the results are easy to understand, which means this could be used in real-life medical diagnosis. |
Keywords
» Artificial intelligence » Deep learning » Loss function » Semantics
