Yan Xu, Jianwen Zhang, Eric Chang, Maode Lai, and Zhuowen Tu
Histopathology image analysis plays a very important role in cancer diagnosis and therapeutic treatment. Existing supervised ap- proaches 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 su- pervision (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 histopathol- ogy images demonstrate the great advantages of ccMIL.
|Published in||International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)|