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Talk · International Symposium on Medical Information Processing and Analysis · November 18, 2025

FSTS-Net: A Feature-mixing aided Semi-supervised Teacher-Student Network for Polyp Image Segmentation

Ritabrata Roy, Utathya Aich, Asya I. Lyanova, Dmitrii Kaplun, Ram Sarkar

Dmitry Kaplun
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Abstract

Polyp segmentation is a critical task in automated colonoscopy analysis, playing a vital role in the early detection and prevention of colorectal cancer. However, the scarcity of pixel-level annotations in medical imaging limits the scalability of fully supervised methods. We introduce FSTS-Net, a feature-mixing aided semi-supervised teacher-student frame-work designed specifically for polyp segmentation. FSTS-Net employs a U-Net style encoder-decoder architecture and integrates an Exponential Moving Average (EMA) teacher to guide a student model through pseudo-labeling and multi-scale consistency learning. The model performs feature-level semantic mixing in latent space, preserving anatomical structure and contextual integrity crucial for polyp detection. Extensive experiments on benchmark datasets, including CVC-Clinic-DB, Kvasir, and Colon-DB, demonstrate that FSTS-Net consistently surpasses existing semi-supervised methods, particularly in low-label settings (15% labeled data), achieving Dice scores of 0.8636, 0.9308, and 0.8249 respectively. By leveraging unlabeled data effectively and maintaining anatomical fidelity through feature-space mixing, FSTS-Net sets a new benchmark for semi-supervised polyp segmentation, with strong potential for clinical use. The code for our proposed framework is available on https://github.com/Utathyaworks/FSTS-Net.

Colorectal Cancer Screening and DetectionMedical Image Segmentation TechniquesAI in cancer detectionpolyp segmentationcolonoscopy imagessemi-supervised learning

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