Xu Yan, Zhang Jianwen, Chang Eric I-Chao, Lai Maode, Tu Zhuowen
State Key Laboratory of Software Development Environment, Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education, Beihang University, China.
Med Image Comput Comput Assist Interv. 2012;15(Pt 3):623-30. doi: 10.1007/978-3-642-33454-2_77.
Histopathology image segmentation plays a very important role in cancer diagnosis and therapeutic treatment. Existing supervised approaches for image segmentation require a large amount of high quality manual delineations (on pixels), which is often hard to obtain. In this paper, we propose a new algorithm along the line of weakly supervised learning; we introduce context constraints as a prior for multiple instance learning (ccMIL), which significantly reduces the ambiguity in weak supervision (a 20% gain); our method utilizes image-level labels to learn an integrated model to perform histopathology cancer image segmentation, clustering, and classification. Experimental results on colon histopathology images demonstrate the great advantages of ccMIL.
组织病理学图像分割在癌症诊断和治疗中起着非常重要的作用。现有的用于图像分割的监督方法需要大量高质量的手动标注（在像素级别），而这往往很难获得。在本文中，我们沿着弱监督学习的思路提出了一种新算法；我们引入上下文约束作为多实例学习的先验（ccMIL），这显著降低了弱监督中的模糊性（提高了20%）；我们的方法利用图像级标签来学习一个集成模型，以执行组织病理学癌症图像分割、聚类和分类。在结肠组织病理学图像上的实验结果证明了ccMIL的巨大优势。
