Utilizing Segment Anything Model For Assessing Localization of GRAD-CAM in Medical Imaging

• URL: http://arxiv.org/abs/2306.15692v1
• Date: Sat, 24 Jun 2023 19:54:50 GMT
• Title: Utilizing Segment Anything Model For Assessing Localization of GRAD-CAM in Medical Imaging
• Authors: Evan Kellener, Ihina Nath, An Ngo, Thomas Nguyen, Joshua Schuman, Coen Adler, Arnav Kartikeya
• Abstract summary: Saliency map algorithms are being applied in multiple fields, including medical imaging. Current works examine capabilities through assessing the localization of saliency maps upon medical abnormalities within an image. We propose utilizing Segment Anything Model (SAM) to both further the accuracy of such existing metrics, while also generalizing beyond the need for human annotations.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: The introduction of saliency map algorithms as an approach for assessing the interoperability of images has allowed for a deeper understanding of current black-box models with Artificial Intelligence. Their rise in popularity has led to these algorithms being applied in multiple fields, including medical imaging. With a classification task as important as those in the medical domain, a need for rigorous testing of their capabilities arises. Current works examine capabilities through assessing the localization of saliency maps upon medical abnormalities within an image, through comparisons with human annotations. We propose utilizing Segment Anything Model (SAM) to both further the accuracy of such existing metrics, while also generalizing beyond the need for human annotations. Our results show both high degrees of similarity to existing metrics while also highlighting the capabilities of this methodology to beyond human-annotation. Furthermore, we explore the applications (and challenges) of SAM within the medical domain, including image pre-processing before segmenting, natural language proposals to SAM in the form of CLIP-SAM, and SAM accuracy across multiple medical imaging datasets.

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