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Automatic Prompt Generation Using Class Activation Maps for Foundational Models: A Polyp Segmentation Case Study
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DOI：10.3390/make7010022.png)
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Abstract
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We introduce a weakly supervised segmentation approach that leverages class activation maps and the Segment Anything Model to generate high-quality masks using only classification data. A pre-trained classifier produces class activation maps that, once thresholded, yield bounding boxes encapsulating the regions of interest. These boxes prompt the SAM to generate detailed segmentation masks, which are then refined by selecting the best overlap with automatically generated masks from the foundational model using the intersection over union metric. In a polyp segmentation case study, our approach outperforms existing zero-shot and weakly supervised methods, achieving a mean intersection over union of 0.63. This method offers an efficient and general solution for image segmentation tasks where segmentation data are scarce.
Keywords:
image segmentation
class activation map
segment anything
annotation tools
zero-shot learning
weakly supervised semantic segmentation
deep learning
medical image processing
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