Pith. sign in

REVIEW 2 cited by

Polyp SAM 2: Advancing Zero shot Polyp Segmentation in Colorectal Cancer Detection

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2408.05892 v4 pith:I7EOV3DM submitted 2024-08-12 eess.IV cs.CVcs.LG

Mobina Mansoori , Sajjad Shahabodini , Jamshid Abouei , Konstantinos N. Plataniotis , Arash Mohammadi This is my paper · ORCID
classification eess.IVcs.CVcs.LG
keywords polypsegmentationcancercolorectaldetectionperformanceaccurateadvance
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Polyp segmentation plays a crucial role in the early detection and diagnosis of colorectal cancer. However, obtaining accurate segmentations often requires labor-intensive annotations and specialized models. Recently, Meta AI Research released a general Segment Anything Model 2 (SAM 2), which has demonstrated promising performance in several segmentation tasks. In this manuscript, we evaluate the performance of SAM 2 in segmenting polyps under various prompted settings. We hope this report will provide insights to advance the field of polyp segmentation and promote more interesting work in the future. This project is publicly available at https://github.com/ sajjad-sh33/Polyp-SAM-2.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

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

  1. HRVVS: A High-resolution Video Vasculature Segmentation Network via Hierarchical Autoregressive Residual Priors

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A new dataset and network for segmenting hepatic vasculature in high-resolution hepatectomy videos, reporting the best scores on the new benchmark.

  2. Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models

    eess.IV 2025-05 conditional novelty 4.0 of 10

    Fine-tuning recent natural-domain foundation models, especially AIMv2, improves medical image classification accuracy across mammography, skin lesion, retinopathy, and chest X-ray benchmarks.

Pith tools