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 (, 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 .

中文翻译：

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通过特征分解进行多类医学图像分割的弱监督学习


具有图像级标签的弱监督学习，将深度学习从高度劳动密集型的像素级注释中解放出来，在医学图像分割领域获得了极大的关注。然而，现有的弱监督方法主要针对单类分割而设计，而对多类医学图像分割的探索很少。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，我们提出了一种新颖的弱监督学习方法，用于具有图像级标签的多类医学图像分割。在多类分类主干方面，提出了一种编码多尺度特征的多级分类网络，与相应的 CAM 一起分别生成每个类的二进制预测。为了解决上述问题（标签共生和位置邻接），首先提出了基于语义亲和力的特征分解模块，通过最大化类间特征距离来学习类独立和类相关特征。通过交叉指导损失来共同利用上述特征，标签共生现象得到了很大程度的缓解。在位置邻接方面，构造了互斥损失以最小化不同类别对应的区域之间的重叠。三个数据集的实验结果证明了所提出的弱监督框架对于单类和多类医学图像分割的优越性能。我们相信本文的分析将为多类别医学图像分割的未来工作提供新的思路。本文的源代码可在 公开获取。