Towards Multi-dimensional Explanation Alignment for Medical Classification
This addresses interpretability challenges in medical image analysis, which has ethical and legal implications, though it appears incremental as it builds on existing interpretable methods.
The paper tackles the lack of interpretability in medical image analysis by proposing Med-MICN, a framework that aligns interpretability across neural symbolic reasoning, concept semantics, and saliency maps, achieving high prediction accuracy and reducing human training effort on four benchmark datasets.
The lack of interpretability in the field of medical image analysis has significant ethical and legal implications. Existing interpretable methods in this domain encounter several challenges, including dependency on specific models, difficulties in understanding and visualization, as well as issues related to efficiency. To address these limitations, we propose a novel framework called Med-MICN (Medical Multi-dimensional Interpretable Concept Network). Med-MICN provides interpretability alignment for various angles, including neural symbolic reasoning, concept semantics, and saliency maps, which are superior to current interpretable methods. Its advantages include high prediction accuracy, interpretability across multiple dimensions, and automation through an end-to-end concept labeling process that reduces the need for extensive human training effort when working with new datasets. To demonstrate the effectiveness and interpretability of Med-MICN, we apply it to four benchmark datasets and compare it with baselines. The results clearly demonstrate the superior performance and interpretability of our Med-MICN.