School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, 430074, China.
Comput Biol Med. 2024 Mar;171:108228. doi: 10.1016/j.compbiomed.2024.108228. Epub 2024 Feb 28.
Weakly supervised learning with image-level labels, releasing deep learning from highly labor-intensive pixel-wise annotation, has gained great attention for medical image segmentation. However, existing weakly supervised methods are mainly designed for single-class segmentation while leaving multi-class medical image segmentation rarely-explored. Different from natural images, label symbiosis, together with location adjacency, are much more common in medical images, making it more challenging for multi-class segmentation. In this paper, we propose a novel weakly supervised learning method for multi-class medical image segmentation with image-level labels. In terms of the multi-class classification backbone, a multi-level classification network encoding multi-scale features is proposed to produce binary predictions, together with the corresponding CAMs, of each class separately. To address the above issues (i.e., label symbiosis and location adjacency), a feature decomposition module based on semantic affinity is first proposed to learn both class-independent and class-dependent features by maximizing the inter-class feature distance. Through a cross-guidance loss to jointly utilize the above features, label symbiosis is largely alleviated. In terms of location adjacency, a mutually exclusive loss is constructed to minimize the overlap among regions corresponding to different classes. Experimental results on three datasets demonstrate the superior performance of the proposed weakly-supervised framework for both single-class and multi-class medical image segmentation. We believe the analysis in this paper would shed new light on future work for multi-class medical image segmentation. The source code of this paper is publicly available at https://github.com/HustAlexander/MCWSS.
基于图像级标签的弱监督学习方法，将深度学习从高劳动密集型像素级注释中解放出来，在医学图像分割中得到了广泛关注。然而，现有的弱监督方法主要是为单类分割而设计的，而很少涉及多类医学图像分割。与自然图像不同，标签共生以及位置邻接在医学图像中更为常见，这使得多类分割更具挑战性。在本文中，我们提出了一种新的基于图像级标签的多类医学图像分割的弱监督学习方法。在多类分类骨干方面，提出了一种多尺度特征编码的多级分类网络，分别对每个类进行二进制预测，并生成相应的 CAM。为了解决上述问题（即标签共生和位置邻接），首先提出了一种基于语义相似性的特征分解模块，通过最大化类间特征距离来学习类独立和类依赖特征。通过交叉指导损失来联合利用上述特征，极大地缓解了标签共生问题。在位置邻接方面，构建了一个互斥损失，以最小化对应不同类别的区域之间的重叠。在三个数据集上的实验结果表明，所提出的弱监督框架在单类和多类医学图像分割中都具有优越的性能。我们相信本文的分析将为未来的多类医学图像分割工作提供新的思路。本文的源代码可在 https://github.com/HustAlexander/MCWSS 上获得。
