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<p>The precise and reliable image detection methods (object detection) play a pivotal role in the early diagnosis of gastrointestinal cancer lesions. However, existing related approaches are limited in extracting multiscale features and inadequately labeled datasets. This study presents a semi-supervised polyp detection via a target-aware image reinforcement strategy and dual-branch contrastive learning (SPD-IRSCL). This model investigates a novel framework that integrates a pre-training module through contrastive learning and introduces a target-aware image reinforcement strategy to enhance predictive performance. Specifically, we employ a dual-encoder network to generate positive and negative sample pairs, enabling the extraction and comparison of fine-grained feature representations for constructing an optimal pre-trained model. Subsequently, we propose an image reinforcement strategy that augments the original datasets by strategically mosaicking targets (polyps) into diverse background images, thus enriching the distribution of training data. Building upon the optimized pre-trained model, we reconstruct a hierarchical detection framework comprising Backbone, Neck, and Head modules. The Backbone module leverages convolutional neural networks to extract multiscale feature representations, while the Neck module employs a primary-auxiliary dual-branch network to facilitate multiscale feature fusion, enabling the integration of different-level semantic information. The Head module utilizes task-specific subnetworks for precise object localization and classification. We verify the SPD-IRSCL model through model comparison, ablation studies, and sensitivity tests, demonstrating superior performance in complex detection scenarios, especially in small-scale medical datasets. The framework synergistically integrates contrastive learning, data reinforcement, and hierarchical feature extraction, showing potential for clinical applications like computer-aided diagnosis, screening, and prevention of cancer, and telehealth services.</p>

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SPD-IRSCL: Semi-supervised Polyp Detection via Target-Aware Image Reinforcement Strategy and Dual-Branch Contrastive Learning

  • 期刊论文

  • 发表日期：

    2025年9月5日

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  • Ziyi Deng,
  • Jia Liu,
  • Miao Zhang,
  • Fang Hu

摘要

The precise and reliable image detection methods (object detection) play a pivotal role in the early diagnosis of gastrointestinal cancer lesions. However, existing related approaches are limited in extracting multiscale features and inadequately labeled datasets. This study presents a semi-supervised polyp detection via a target-aware image reinforcement strategy and dual-branch contrastive learning (SPD-IRSCL). This model investigates a novel framework that integrates a pre-training module through contrastive learning and introduces a target-aware image reinforcement strategy to enhance predictive performance. Specifically, we employ a dual-encoder network to generate positive and negative sample pairs, enabling the extraction and comparison of fine-grained feature representations for constructing an optimal pre-trained model. Subsequently, we propose an image reinforcement strategy that augments the original datasets by strategically mosaicking targets (polyps) into diverse background images, thus enriching the distribution of training data. Building upon the optimized pre-trained model, we reconstruct a hierarchical detection framework comprising Backbone, Neck, and Head modules. The Backbone module leverages convolutional neural networks to extract multiscale feature representations, while the Neck module employs a primary-auxiliary dual-branch network to facilitate multiscale feature fusion, enabling the integration of different-level semantic information. The Head module utilizes task-specific subnetworks for precise object localization and classification. We verify the SPD-IRSCL model through model comparison, ablation studies, and sensitivity tests, demonstrating superior performance in complex detection scenarios, especially in small-scale medical datasets. The framework synergistically integrates contrastive learning, data reinforcement, and hierarchical feature extraction, showing potential for clinical applications like computer-aided diagnosis, screening, and prevention of cancer, and telehealth services.