通过交叉可靠知识蒸馏的半监督医学图像分割

摘要(中文译文)

半监督医学图像分割旨在通过利用无标注数据来缓解对大规模标注的依赖。然而，现有方法往往缺乏有效机制，既无法充分利用有标注数据中的语义知识，也难以在无标注扰动分支之间实现可靠的知识交互。为解决这一问题，我们提出了交叉可靠知识蒸馏（Cross Reliable Knowledge Distillation，CRKD），一种面向医学图像的半监督框架。具体而言，我们首先引入类别语义蒸馏（Class Semantic Distillation，CSD）模块，通过特征空间中的类别原型对齐，将有标注数据中的类别级语义知识迁移至无标注表示，促进判别性特征学习。随后，我们提出低熵一致性（Low-Entropy Consistency，LEC）模块，动态强调可靠的低熵区域，提升多样扰动下像素级预测的一致性。最后，我们设计了交叉知识蒸馏（Cross Knowledge Distillation，CKD）模块，由交叉像素知识蒸馏（Cross Pixel Knowledge Distillation，CPKD）和交叉特征知识蒸馏（Cross Feature Knowledge Distillation，CFKD）组成，能够在特征空间和像素空间中实现可靠性感知的双向知识迁移，使扰动分支在不引入额外网络架构的情况下协同修正不确定的表示与预测。大量实验证明了CRKD的有效性，在多种成像模态和病变类型上均取得了显著的性能提升和稳健的泛化能力。代码可在 https://github.com/eeaesa/CRKD 获取。

Abstract

Semi-supervised medical image segmentation aims to alleviate the dependence on large-scale annotations by exploiting unlabeled data. However, existing methods often lack effective mechanisms for leveraging semantic knowledge from labeled data and enabling reliable knowledge interaction among unlabeled perturbation branches. To address this, we propose Cross Reliable Knowledge Distillation (CRKD), a semi-supervised framework for medical images. Specifically, we first introduce a Class Semantic Distillation (CSD) module, which transfers class-level semantic knowledge from labeled data to unlabeled representations through class prototype alignment in the feature space, facilitating discriminative feature learning. Then, we propose a Low-Entropy Consistency (LEC) module that dynamically emphasizes reliable low-entropy regions, improving pixel-level prediction consistency under diverse perturbations. Finally, we develop a Cross Knowledge Distillation (CKD) module, consisting of Cross Pixel Knowledge Distillation (CPKD) and Cross Feature Knowledge Distillation (CFKD), which enables reliability-aware bidirectional knowledge transfer in both feature and pixel spaces, allowing perturbation branches to collaboratively correct uncertain representations and predictions without introducing additional network architectures. Extensive experiments demonstrate the effectiveness of CRKD, achieving significant performance gains and robust generalization across diverse imaging modalities and lesion types. Code is available at https://github.com/eeaesa/CRKD.

如何引用

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 的其他文献

• 面向开放世界半监督肝脏病变识别的OOD感知可靠性学习
• SOM-GAN：一种用于非配对医学图像合成的结构保持一对多生成对抗网络
• 在跨域医学图像分割中，通过特征增强的加权平均实现连续的测试时间自适应