Me-NDT: Neural-backed Decision Tree for Visual Explainability of Deep Medical ModelsDownload PDF

Apr 06, 2021 (edited Jun 01, 2021)MIDL 2021 Conference Short SubmissionReaders: Everyone
  • Keywords: Model interpretability, Medical image analysis
  • Abstract: Despite the progress of deep learning on medical imaging, there is still not a true understanding of what networks learn and of how decisions are reached. Here, we address this by proposing a Visualized Neural-backed Decision Tree for Medical image analysis, Me-NDT. It is a CNN with a tree-based structure template that allows for both classification and visualization of firing neurons, thus offering interpretability. We also introduce node and path losses that allow Me-NDT to consider the entire path instead of isolated nodes. Our experiments on brain CT and chest radiographs outperform all baselines. Overall, Me-NDT is a lighter, comprehensively explanatory model, of great value for clinical practice.
  • Paper Type: both
  • Primary Subject Area: Interpretability and Explainable AI
  • Secondary Subject Area: Detection and Diagnosis
  • Paper Status: original work, not submitted yet
  • Source Code Url: Our code needs to be sorted out before it can be made public.
  • Data Set Url: Brain CT (provided by RSNA challenge): https://www.kaggle.com/c/rsna-intracranial-hemorrhage-detection/overview
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  • Authorship: I confirm that I am the author of this work and that it has not been submitted to another publication before.
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