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