解决医学影像分割中的类别不平衡问题，提升小器官分割精度。
Semantic Class Distribution Learning for Debiasing Semi-Supervised Medical Image Segmentation
- 通过学习类别条件特征分布，缓解标注与表征偏差。
- 在Synapse和AMOS数据集上显著提升整体与小类别的分割性能。
- 适合关注医学图像分割中少数类表现的科研人员使用。
医学图像分割对辅助诊断至关重要，但密集像素级标注耗时且成本高，且医学数据集普遍存在严重类别不平衡问题。这种不平衡导致少数类在特征表示中被主导类淹没，阻碍判别性特征的学习，使可靠分割尤为困难。为此，本文提出语义类别分布学习（SCDL）框架，一种可即插即用的模块，通过学习结构化的类别条件特征分布来减轻监督与表征偏差。SCDL结合类别分布双向对齐（CDBA），将嵌入与可学习类别代理对齐，并利用语义锚点约束（SAC）通过标注数据引导代理。在Synapse和AMOS数据集上的实验表明，SCDL显著提升了整体与类别级别的分割性能，尤其在多个低频器官上取得明显进步。匿名代码已发布于https://anonymous.4open.science/r/SCDL。
原文摘要 · Abstract (English)
Medical image segmentation is critical for computer-aided diagnosis. However, dense pixel-level annotation is time-consuming and costly, and medical datasets often exhibit severe class imbalance. Such an imbalance causes minority structures to be overwhelmed by dominant classes in feature representations, hindering the learning of discriminative features and making reliable segmentation particularly challenging. To address this, we propose the Semantic Class Distribution Learning (SCDL) framework, a plug-and-play module that mitigates supervision and representation biases by learning structured class-conditional feature distributions. SCDL integrates Class Distribution Bidirectional Alignment (CDBA) to align embeddings with learnable class proxies and leverages Semantic Anchor Constraints (SAC) to guide proxies using labeled data. Experiments on the Synapse and AMOS datasets demonstrate that SCDL largely improves segmentation performance across both overall and class-level metrics, with particularly notable gains for several low-frequency organs. Our anonymous code is released at https://anonymous.4open.science/r/SCDL.
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