BARL：表示空间和标签空间的双侧对齐用于半监督体积医学图像分割
BARL: Bilateral Alignment in Representation and Label Spaces for Semi-Supervised Volumetric Medical Image Segmentation
AI总结：
BARL通过双侧对齐表示空间和标签空间，提升半监督体积医学图像分割的性能。
摘要：
半监督医学图像分割（SSMIS）旨在在大幅减少标注成本的同时实现全监督性能。主流SSMIS方法依赖于标签空间一致性，但忽略了同样关键的表示空间对齐。没有和谐潜在特征，模型难以学习既具有判别力又在空间上连贯的表示。为此，我们引入了表示空间和标签空间的双侧对齐（BARL），一个统一的框架，结合两个协作分支并强制在两个空间中对齐。对于标签空间对齐，受协同训练和多尺度解码的启发，我们设计了双路径正则化（DPR）和渐进认知偏差校正（PCBC），以施加细粒度跨分支一致性，同时缓解从粗到细尺度的误差积累。对于表示空间对齐，我们进行分支间的区域级和病变实例匹配，明确捕捉医学影像中常见的碎片化、复杂的病理模式。在四个公开基准和一个专有CBCT数据集上的大量实验表明，BARL在很大程度上超越了最先进的SSMIS方法。消融研究进一步验证了每个组件的贡献。代码将很快发布。
英文摘要：
Semi-supervised medical image segmentation (SSMIS) seeks to match fully supervised performance while sharply reducing annotation cost. Mainstream SSMIS methods rely on \emph{label-space consistency}, yet they overlook the equally critical \emph{representation-space alignment}. Without harmonizing latent features, models struggle to learn representations that are both discriminative and spatially coherent. To this end, we introduce \textbf{Bilateral Alignment in Representation and Label spaces (BARL)}, a unified framework that couples two collaborative branches and enforces alignment in both spaces. For label-space alignment, inspired by co-training and multi-scale decoding, we devise \textbf{Dual-Path Regularization (DPR)} and \textbf{Progressively Cognitive Bias Correction (PCBC)} to impose fine-grained cross-branch consistency while mitigating error accumulation from coarse to fine scales. For representation-space alignment, we conduct region-level and lesion-instance matching between branches, explicitly capturing the fragmented, complex pathological patterns common in medical imagery. Extensive experiments on four public benchmarks and a proprietary CBCT dataset demonstrate that BARL consistently surpasses state-of-the-art SSMIS methods. Ablative studies further validate the contribution of each component. Code will be released soon.
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