Quantitative Explainability Study of Deformable Convolutional Neural Networks using Chest X-rays

Traditional convolutional neural networks (tCNNs) often struggle with medical image analysis due to complex transformations and irregular structures with weak boundaries. Deformable convolutional neural networks (dCNNs) address these challenges through specialized modules, showing improvements in classification, segmentation, and explainability on general image tasks. However, dCNNs remain relatively unexplored in the medical domain. This study provides a unique quantitative comparison of explainability between tCNNs and dCNNs on medical image classification tasks, focusing on lung disease classification using chest X-rays (CXRs). We tested four models with varying degrees of deformability and generated saliency maps using Guided GradCAM. To quantify interpretability, we introduced a novel metric: continuous intersection over union (cIoU). While tCNNs demonstrated slightly better classification results, dCNNs showed significant improvements in explainability. We observed a positive correlation between the number of deformable layers in a model and the quality of its saliency maps. These findings highlight the potential of dCNNs in enhancing the interpretability of medical image analysis, which is crucial for real-world clinical research and practice.