Polyp-SAM++: Can A Text Guided SAM Perform Better for Polyp Segmentation?
This work addresses polyp segmentation for colorectal cancer diagnosis, but it appears incremental as it adapts an existing general-purpose model with text guidance.
The study tackled polyp segmentation in medical images by proposing Polyp-SAM++, a text prompt-aided version of the Segment Anything Model (SAM), and found it improved robustness and precision compared to unprompted SAM on benchmark datasets.
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++.