Automatic diagnosis based on medical imaging necessitates both lesion segmentation and disease classification. Lesion segmentation requires pixel-level annotations while disease classification only requires image-level annotations. The two tasks are usually studied separately despite the latter problem relies on the former. Motivated by the close correlation between them, we propose a mixed-supervision guided method and a residual-aided classification U-Net model (ResCU-Net) for joint segmentation and benign-malignant classification. By coupling the strong supervision in the form of segmentation mask and weak supervision in the form of benign-malignant label through a simple annotation procedure, our method efficiently segments tumor regions while simultaneously predicting a discriminative map for identifying the benign-malignant types of tumors. Our network, ResCU-Net, extends U-Net by incorporating the residual module and the SegNet architecture to exploit multilevel information for achieving improved tissue identification. With experiments on a public mammogram database of INbreast, we validate the effectiveness of our method and achieve consistent improvements over state-of-the-art models.

中文翻译：

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使用混合监督引导深度模型从乳房 X 光照片中同时分割和分类肿块区域

基于医学影像的自动诊断需要病灶分割和疾病分类。病变分割需要像素级注释，而疾病分类只需要图像级注释。尽管后一个问题依赖于前一个，但通常分别研究这两个任务。受它们之间密切相关性的启发，我们提出了一种混合监督引导方法和一种残差辅助分类 U-Net 模型 (ResCU-Net)，用于联合分割和良恶性分类。通过通过简单的注释程序将分割掩码形式的强监督和良恶性标签形式的弱监督结合起来，我们的方法有效地分割了肿瘤区域，同时预测了用于识别肿瘤良恶性类型的判别图。我们的网络 ResCU-Net 通过结合残差模块和 SegNet 架构来扩展 U-Net，以利用多级信息来实现改进的组织识别。通过对 INbreast 公共乳房 X 光照片数据库的实验，我们验证了我们方法的有效性，并在最先进的模型上实现了一致的改进。