No Masks Needed: Explainable AI for Deriving Segmentation from Classification

• URL: http://arxiv.org/abs/2508.04534v1
• Date: Wed, 06 Aug 2025 15:18:00 GMT
• Title: No Masks Needed: Explainable AI for Deriving Segmentation from Classification
• Authors: Mosong Ma, Tania Stathaki, Michalis Lazarou,
• Abstract summary: We introduce a novel approach that fine-tunes pre-trained models specifically for medical images.<n>Our method integrates Explainable AI to generate relevance scores, enhancing the segmentation process.<n>Unlike traditional methods that excel in standard benchmarks but falter in medical applications, our approach achieves improved results on datasets like CBIS-DDSM, NuInsSeg and Kvasir-SEG.
• Score: 6.647179199462945
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Medical image segmentation is vital for modern healthcare and is a key element of computer-aided diagnosis. While recent advancements in computer vision have explored unsupervised segmentation using pre-trained models, these methods have not been translated well to the medical imaging domain. In this work, we introduce a novel approach that fine-tunes pre-trained models specifically for medical images, achieving accurate segmentation with extensive processing. Our method integrates Explainable AI to generate relevance scores, enhancing the segmentation process. Unlike traditional methods that excel in standard benchmarks but falter in medical applications, our approach achieves improved results on datasets like CBIS-DDSM, NuInsSeg and Kvasir-SEG.

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