Details

Autor(en) / Beteiligte

• Jia, Zhipeng
• Huang, Xingyi
• Chang, Eric I-Chao
• Xu, Yan

Titel

Constrained Deep Weak Supervision for Histopathology Image Segmentation

Ist Teil von

• IEEE transactions on medical imaging, 2017-11, Vol.36 (11), p.2376-2388

Ort / Verlag

United States: IEEE

Erscheinungsjahr

2017

Quelle

IEEE Xplore Digital Library

Beschreibungen/Notizen

• In this paper, we develop a new weakly supervised learning algorithm to learn to segment cancerous regions in histopathology images. This paper is under a multiple instance learning (MIL) framework with a new formulation, deep weak supervision (DWS); we also propose an effective way to introduce constraints to our neural networks to assist the learning process. The contributions of our algorithm are threefold: 1) we build an end-to-end learning system that segments cancerous regions with fully convolutional networks (FCNs) in which image-to-image weakly-supervised learning is performed; 2) we develop a DWS formulation to exploit multi-scale learning under weak supervision within FCNs; and 3) constraints about positive instances are introduced in our approach to effectively explore additional weakly supervised information that is easy to obtain and enjoy a significant boost to the learning process. The proposed algorithm, abbreviated as DWS-MIL, is easy to implement and can be trained efficiently. Our system demonstrates the state-of-the-art results on large-scale histopathology image data sets and can be applied to various applications in medical imaging beyond histopathology images, such as MRI, CT, and ultrasound images.