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Polyp-SAM++: Can A Text Guided SAM Perform Better for Polyp Segmentation?

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arxiv 2308.06623 v1 pith:KV65EC44 submitted 2023-08-12 eess.IV cs.CV

Risab Biswas This is my paper
classification eess.IVcs.CV
keywords segmentationpolypwillbettermodelpolyp-samtextfield
verification ladder T0 review T1 audit T2 compute T3 formal
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Meta recently released SAM (Segment Anything Model) which is a general-purpose segmentation model. SAM has shown promising results in a wide variety of segmentation tasks including medical image segmentation. In the field of medical image segmentation, polyp segmentation holds a position of high importance, thus creating a model which is robust and precise is quite challenging. Polyp segmentation is a fundamental task to ensure better diagnosis and cure of colorectal cancer. As such in this study, we will see how Polyp-SAM++, a text prompt-aided SAM, can better utilize a SAM using text prompting for robust and more precise polyp segmentation. We will evaluate the performance of a text-guided SAM on the polyp segmentation task on benchmark datasets. We will also compare the results of text-guided SAM vs unprompted SAM. With this study, we hope to advance the field of polyp segmentation and inspire more, intriguing research. The code and other details will be made publically available soon at https://github.com/RisabBiswas/Polyp-SAM++.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. MSA2-Net: Utilizing Self-Adaptive Convolution Module to Extract Multi-Scale Information in Medical Image Segmentation

    cs.CV 2025-09 reject novelty 4.0 of 10

    MSA2-Net proposes a dataset-adaptive convolution module for multi-scale medical image segmentation and reports strong Dice scores, but key definitions and one abstract number conflict with the experiments.

  2. Prompt Mechanisms in Medical Imaging: A Comprehensive Survey

    eess.IV 2025-06 conditional novelty 4.0 of 10

    A broad survey that organizes prompt mechanisms for medical image generation, segmentation, and classification into a two-dimensional taxonomy of core technologies and clinical applications.

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