Polyp-SES: Automatic Polyp Segmentation with Self-Enriched Semantic Model

• URL: http://arxiv.org/abs/2410.01210v1
• Date: Wed, 2 Oct 2024 03:34:23 GMT
• Title: Polyp-SES: Automatic Polyp Segmentation with Self-Enriched Semantic Model
• Authors: Quang Vinh Nguyen, Thanh Hoang Son Vo, Sae-Ryung Kang, Soo-Hyung Kim,
• Abstract summary: We propose an innovative method named Automatic Polyp with Self-Enriched Semantic Model'' to address limitations in existing approaches. First, we extract a sequence of features from an input image and decode high-level features to generate an initial segmentation mask. Using the proposed self-enriched semantic module, we query potential semantics and augment deep features with additional semantics, thereby aiding the model in understanding context more effectively.
• Score: 7.424888086388194
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automatic polyp segmentation is crucial for effective diagnosis and treatment in colonoscopy images. Traditional methods encounter significant challenges in accurately delineating polyps due to limitations in feature representation and the handling of variability in polyp appearance. Deep learning techniques, including CNN and Transformer-based methods, have been explored to improve polyp segmentation accuracy. However, existing approaches often neglect additional semantics, restricting their ability to acquire adequate contexts of polyps in colonoscopy images. In this paper, we propose an innovative method named ``Automatic Polyp Segmentation with Self-Enriched Semantic Model'' to address these limitations. First, we extract a sequence of features from an input image and decode high-level features to generate an initial segmentation mask. Using the proposed self-enriched semantic module, we query potential semantics and augment deep features with additional semantics, thereby aiding the model in understanding context more effectively. Extensive experiments show superior segmentation performance of the proposed method against state-of-the-art polyp segmentation baselines across five polyp benchmarks in both superior learning and generalization capabilities.

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