Attri-Net: A Globally and Locally Inherently Interpretable Model for Multi-Label Classification Using Class-Specific Counterfactuals

The development of Attri-Net highlights the growing importance of interpretability in AI, especially in medical contexts where decisions can be life-altering. Similar to the RL-U$^2$Net model, which enhances segmentation accuracy through multimodal feature fusion, Attri-Net aims to improve the clarity of model predictions. Both models reflect a trend towards integrating advanced methodologies to enhance diagnostic precision. Furthermore, the integration of clinical knowledge into Attri-Net's explanations aligns with the ongoing discourse on the necessity of interpretability in AI, as seen in the exploration of backward stochastic differential equations in learning PDEs, which also emphasizes the need for clear, understandable models.