G1 - Me-NDT: Neural-backed Decision Tree for Visual Explainability of Deep Medical Models
Guanghui FU, Ruiqian Wang, Jianqiang Li, Maria Vakalopoulou, Vicky Kalogeiton
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.
Thursday 8th July
G1-9 (short): Interpretability and Explainable AI - 16:45 - 17:30 (UTC+2)