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<p>Accurate gland segmentation in colorectal cancer histopathology is crucial, but the scarcity of pixel-level annotations limits robust model development. This study aims to develop a highly accurate gland segmentation method that leverages weakly labeled data, specifically image-level labels, to overcome the need for extensive pixel-level annotations. We propose a novel three-stage framework that uniquely combines self-supervised fine-tuning of the DINOv2 vision transformer, attention-based pseudo-label generation, and a boundary-aware loss function. Initially, an off-the-shelf DINOv2 encoder is fine-tuned on a large unlabeled dataset of histopathology images. This fine-tuned encoder is then integrated into a classification network equipped with an attention mechanism, which is trained using image-level labels to generate initial pseudo-labels via attention maps. These maps are refined through blending, thresholding, and Conditional Random Field (CRF) post-processing. Finally, a segmentation network, employing the same fine-tuned encoder and a lightweight decoder, is trained using these refined pseudo-labels and a boundary-aware loss. Ablation studies demonstrated the significant benefit of the fine-tuned encoder and the comprehensive post-processing steps for pseudo-label generation. Further studies confirmed the effectiveness of the boundary-aware loss in improving segmentation accuracy. Our method achieved superior performance on the GlaS dataset compared to several state-of-the-art methods, including both fully supervised and weakly supervised approaches, demonstrating higher F1-score, Object Dice, and lower Object Hausdorff distance. This approach effectively addresses the challenge of limited pixel-level annotations by utilizing more readily available image-level data, offering a promising solution for improved colorectal cancer diagnosis. The proposed framework shows potential for generalization to other histopathology image analysis tasks.</p>

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Weakly supervised colorectal gland segmentation through self-supervised learning and attention-based pseudo-labeling

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月19日

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  • Huer Wen,
  • Yan Wu,
  • DeShuang Huang,
  • Cong Liu

摘要

Accurate gland segmentation in colorectal cancer histopathology is crucial, but the scarcity of pixel-level annotations limits robust model development. This study aims to develop a highly accurate gland segmentation method that leverages weakly labeled data, specifically image-level labels, to overcome the need for extensive pixel-level annotations. We propose a novel three-stage framework that uniquely combines self-supervised fine-tuning of the DINOv2 vision transformer, attention-based pseudo-label generation, and a boundary-aware loss function. Initially, an off-the-shelf DINOv2 encoder is fine-tuned on a large unlabeled dataset of histopathology images. This fine-tuned encoder is then integrated into a classification network equipped with an attention mechanism, which is trained using image-level labels to generate initial pseudo-labels via attention maps. These maps are refined through blending, thresholding, and Conditional Random Field (CRF) post-processing. Finally, a segmentation network, employing the same fine-tuned encoder and a lightweight decoder, is trained using these refined pseudo-labels and a boundary-aware loss. Ablation studies demonstrated the significant benefit of the fine-tuned encoder and the comprehensive post-processing steps for pseudo-label generation. Further studies confirmed the effectiveness of the boundary-aware loss in improving segmentation accuracy. Our method achieved superior performance on the GlaS dataset compared to several state-of-the-art methods, including both fully supervised and weakly supervised approaches, demonstrating higher F1-score, Object Dice, and lower Object Hausdorff distance. This approach effectively addresses the challenge of limited pixel-level annotations by utilizing more readily available image-level data, offering a promising solution for improved colorectal cancer diagnosis. The proposed framework shows potential for generalization to other histopathology image analysis tasks.