Most medical image lesion segmentation methods rely on hand-crafted accurate annotations of the original image for supervised learning. Recently, a series of weakly supervised or unsupervised methods have been proposed to reduce the dependence on pixel-level annotations. However, these methods are essentially based on pixel-level annotation, ignoring the image-level diagnostic results of the current massive medical images. In this paper, we propose a dual U-shaped two-stage framework that utilizes image-level labels to prompt the segmentation. In the first stage, we pre-train a classification network with image-level labels, which is used to obtain the hierarchical pyramid features and guide the learning of downstream branches. In the second stage, we feed the hierarchical features obtained from the classification branch into the downstream branch through short-skip and long-skip and get the lesion masks under the supervised learning of pixel-level labels. Experiments show that our framework achieves better results than networks simply using pixel-level annotations.

翻译：大多数医学图像病灶分割方法依赖于对原始图像进行手工精确标注以实现监督学习。近期，一系列弱监督或无监督方法被提出以减少对像素级标注的依赖。然而，这些方法本质上仍基于像素级标注，忽略了当前海量医学图像所具备的图像级诊断结果。本文提出一种双U形两阶段框架，利用图像级标签来提示分割过程。在第一阶段，我们使用图像级标签预训练一个分类网络，该网络用于获取层次化金字塔特征并指导下游分支的学习。在第二阶段，我们将从分类分支获得的层次化特征通过短跳跃连接和长跳跃连接馈送至下游分支，并在像素级标签的监督学习下获得病灶掩码。实验表明，与仅使用像素级标注的网络相比，本框架取得了更优的结果。