ILAM: Cross-Fusion of Latent and Attention Features for Explainable Medical Image Classification.

Accurate and interpretable AI models play a critical role in medical image analysis. However, despite advancements in explainable AI (XAI), existing methods struggle with inconsistent interpretability. To overcome this limitation, we introduce the Integrated Latent and Attention Mapping (ILAM) framework, which enhances both classification accuracy and explainability by fusing local and global feature representations.