Relational Explainability of Brain Tumor MRI: Linking Classification Attention to Tumor Masks
This paper proposes a novel relational explainability framework that quantitatively validates the spatial correspondence between Grad-CAM++ classification attention maps and segmented tumor regions in brain MRI, revealing that while attention maps achieve substantial tumor coverage, they exhibit lower spatial precision, thereby demonstrating their complementary role to anatomical segmentation for enhancing the transparency and trustworthiness of medical AI.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
Drowning in papers in your field?
Get daily digests of the most novel papers matching your research keywords — with technical summaries, in your language.