LowRank-CAM: A Computationally Efficient and Interpretable Framework for Medical Image Analysis (Student Abstract)

摘要

Deep learning has advanced medical imaging, but limited interpretability hinders clinical adoption. Class activation maps (CAM) provide visual explanations, yet methods such as Score-CAM are computationally expensive, requiring a forward pass for each activation map and limiting real-time applicability despite their high fidelity. To overcome this limitation, LowRank-CAM is proposed, which aggregates activation maps into a global matrix and applies singular value decomposition (SVD) to extract dominant spatial modes. The resulting top-r low-rank attention masks, with r