发表机构
Key Laboratory of Nondestructive Testing, Ministry of Education, Nanchang Hangkong University（南昌航空大学无损检测技术教育部重点实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对医学图像分割标注有限问题，提出无教师单网络框架OFD-Net。通过正交特征解缠模块和引导模块，有效减少误差积累。并开发可靠性感知伪标签学习机制，降低不可靠区域权重。实验验证其有效性，建立了高效可靠的训练范式。
AI 中文摘要
半监督学习是解决医学图像分割标注有限问题的有效方法。现有方法主要依赖师生监督或跨网络一致性生成伪标签，缺乏判断伪标签质量的明确结构参考，低质量伪标签会导致训练不可靠、误差积累等问题。为此提出OFD-Net，一种无教师单网络框架。它采用正交特征解缠模块将无标签数据解缠为背景和前景表示，有效减少误差积累并缓解确认偏差。具体通过解缠引导模块将前景-背景结构先验注入解码器，输出前景表示更清晰的预测。基于此进一步开发可靠性感知伪标签学习机制，根据主预测和解缠的前景-背景响应之间的结构一致性评估无标签监督，在训练中降低不可靠区域权重。在四个公共医学图像分割基准上的大量实验验证了OFD-Net的有效性，结果表明正交前景-背景解缠使OFD-Net在无教师单网络框架内建立了高效可靠的训练范式。
英文摘要
Semi-supervised learning (SSL) is an effective solution for medical image segmentation with limited annotations. Existing SSL methods mainly rely on pseudo-labels generated by teacher-student supervision or cross-network consistency. However, these methods lack an explicit structural reference for judging pseudo-label quality. Low-quality pseudo-labels may lead to unreliable training, error accumulation and confirmation bias when processing unlabeled data with substantial appearance variations. To address this issue, we proposed OFD-Net, a teacher-free single-network framework for reliable semi-supervised medical image segmentation. OFD-Net employs an Orthogonal Feature Disentanglement Module (OFDM) to capture OFD features for reliable SSL by disentangling unlabeled data into background and foreground representations with a reliable structural distribution, thereby effectively reducing error accumulation and alleviating confirmation bias among unlabeled data. Specifically, OFD-Net explicitly employs a Disentanglement Guidance Module (DGM) to inject the resulting structural priors of foreground-background into the decoder by deformable convolution processing, and outputs predictions with clearer foreground representations. Based on DGM and the OFDM, we further develop a reliability-aware pseudo-label learning mechanism that evaluates unlabeled supervision according to the structural consistency between the main prediction and the disentangled foreground-background responses, and then down-weights unreliable regions during training. Extensive experiments on four public medical image segmentation benchmarks, namely ISIC-2016, Kvasir-SEG, Synapse, and ACDC, validate the effectiveness of OFD-Net. These results confirm that orthogonal foreground-background disentanglement enables OFD-Net to establish an efficient and reliable training paradigm within a teacher-free single-network framework.
