论文标题
深度兼容的学习，用于部分监督医学图像细分
Deep Compatible Learning for Partially-Supervised Medical Image Segmentation
论文作者
论文摘要
由于缺乏未标记的结构的监督，部分监督的学习对于细分可能是具有挑战性的，并且直接应用完全监督学习的方法可能会导致不兼容，这意味着地面真相不在鉴于损失功能的优化问题集合中。为了应对挑战，我们提出了一个深层兼容学习（DCL）框架，该框架使用仅带有部分结构的图像来训练单个多标签分割网络。我们首先将部分监督的分割制定为与缺少标签兼容的优化问题，并证明其兼容性。然后，我们为模型配备有条件的分割策略，以将标签从多个部分注销的图像传播到目标。此外，我们提出了一种双重学习策略，该策略同时学习了标签传播的两个相反的映射，以为未标记的结构提供实质性的监督。这两种策略分别称为兼容形式，分别称为条件兼容性和双重兼容性。我们显示该框架通常适用于常规损失功能。该方法对现有方法具有重大的性能提高，尤其是在只有小型培训数据集的情况下。三个分割任务的结果表明，所提出的框架可以实现匹配完全监督模型的性能。
Partially-supervised learning can be challenging for segmentation due to the lack of supervision for unlabeled structures, and the methods directly applying fully-supervised learning could lead to incompatibility, meaning ground truth is not in the solution set of the optimization problem given the loss function. To address the challenge, we propose a deep compatible learning (DCL) framework, which trains a single multi-label segmentation network using images with only partial structures annotated. We first formulate the partially-supervised segmentation as an optimization problem compatible with missing labels, and prove its compatibility. Then, we equip the model with a conditional segmentation strategy, to propagate labels from multiple partially-annotated images to the target. Additionally, we propose a dual learning strategy, which learns two opposite mappings of label propagation simultaneously, to provide substantial supervision for unlabeled structures. The two strategies are formulated into compatible forms, termed as conditional compatibility and dual compatibility, respectively. We show this framework is generally applicable for conventional loss functions. The approach attains significant performance improvement over existing methods, especially in the situation where only a small training dataset is available. Results on three segmentation tasks have shown that the proposed framework could achieve performance matching fully-supervised models.
