Version 1
: Received: 7 May 2024 / Approved: 7 May 2024 / Online: 7 May 2024 (13:58:57 CEST)
How to cite:
Cai, T.; Yan, H.; Ding, K.; Zhang, Y.; Zhou, Y. WSPoly-SAM: Weakly-Supervised and Self-Guided Fine-Tuning of SAM for Colonoscopy Polyp Segmentation. Preprints2024, 2024050389. https://doi.org/10.20944/preprints202405.0389.v1
Cai, T.; Yan, H.; Ding, K.; Zhang, Y.; Zhou, Y. WSPoly-SAM: Weakly-Supervised and Self-Guided Fine-Tuning of SAM for Colonoscopy Polyp Segmentation. Preprints 2024, 2024050389. https://doi.org/10.20944/preprints202405.0389.v1
Cai, T.; Yan, H.; Ding, K.; Zhang, Y.; Zhou, Y. WSPoly-SAM: Weakly-Supervised and Self-Guided Fine-Tuning of SAM for Colonoscopy Polyp Segmentation. Preprints2024, 2024050389. https://doi.org/10.20944/preprints202405.0389.v1
APA Style
Cai, T., Yan, H., Ding, K., Zhang, Y., & Zhou, Y. (2024). WSPoly-SAM: Weakly-Supervised and Self-Guided Fine-Tuning of SAM for Colonoscopy Polyp Segmentation. Preprints. https://doi.org/10.20944/preprints202405.0389.v1
Chicago/Turabian Style
Cai, T., Yan Zhang and Yueyue Zhou. 2024 "WSPoly-SAM: Weakly-Supervised and Self-Guided Fine-Tuning of SAM for Colonoscopy Polyp Segmentation" Preprints. https://doi.org/10.20944/preprints202405.0389.v1
Abstract
Ensuring precise segmentation of colorectal polyps holds critical importance in the early diagnosis and treatment of colorectal cancer. Nevertheless, existing deep learning-based segmentation methods are fully-supervised, requiring extensive precise manual pixel-level annotation data, which leads to high annotation costs. Additionally, it remains challenging to train large-scale segmentation models when confronted with limited colonoscopy data. To address these issues, we introduce the general segmentation foundation model—Segment Anything Model (SAM) into the field of medical image segmentation. Fine-tuning the foundation model is an effective approach to tackle sample scarcity. However, current SAM fine-tuning techniques still rely on precise annotations. To overcome this limitation, we propose WSPoly-SAM, a novel weakly-supervised approach for colonoscopy polyp segmentation. WSPoly-SAM utilizes weak annotations to guide SAM in generating segmentation masks, which are then treated as pseudo-labels to guide the fine-tuning of SAM, thereby reducing the dependence on precise annotations data. To improve the reliability and accuracy of pseudo-labels, we have designed a series of enhancement strategies to improve the quality of pseudo-labels and mitigate the negative impact of low-quality pseudo-labels. Experimental results on five medical image datasets demonstrate that WSPoly-SAM outperforms current fully-supervised mainstream polyp segmentation networks on the Kvasir-SEG, ColonDB, CVC-300, and ETIS datasets. Furthermore, by using different amounts of training data in weakly-supervised and fully-supervised experiments, it is found that weakly-supervised fine-tuning can save 70% to 73% of annotation time costs compared to fully-supervised fine-tuning. This study provides a new perspective on the combination of weakly-supervised learning and SAM models, significantly reducing annotation time and offering insights for further development in the field of colonoscopy polyp segmentation.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
