Weakly supervised histopathology cancer image segmentation and classification

• We propose a new learning method, multiple clustered instance learning (MCIL), along the line of weakly supervised learning.
• The proposed MCIL simultaneously performs image classification (cancer vs. non-cancer image), segmentation, and clustering.
• We embed clustering into MIL and derive a principled solution to performing the three tasks in an integrated framework.
• We introduce contextual constraints as a prior for MCIL, which significantly reduces the ambiguity in multiple instance learning.

Labeling a histopathology image as having cancerous regions or not is a critical task in cancer diagnosis; it is also clinically important to segment the cancer tissues and cluster them into various classes. Existing supervised approaches for image classification and segmentation require detailed manual annotations for the cancer pixels, which are time-consuming to obtain. In this paper, we propose a new learning method, multiple clustered instance learning (MCIL) (along the line of weakly supervised learning) for histopathology image segmentation. The proposed MCIL method simultaneously performs image-level classification (cancer vs. non-cancer image), medical image segmentation (cancer vs. non-cancer tissue), and patch-level clustering (different classes). We embed the clustering concept into the multiple instance learning (MIL) setting and derive a principled solution to performing the above three tasks in an integrated framework. In addition, we introduce contextual constraints as a prior for MCIL, which further reduces the ambiguity in MIL. Experimental results on histopathology colon cancer images and cytology images demonstrate the great advantage of MCIL over the competing methods.