- 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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