Yan Xu, Jun-Yan Zhu, Eric Chang, and Zhuowen Tu
Cancer tissues in histopathology images exhibit abnormal
patterns; it is of great clinical importance to label a
histopathology image as having cancerous regions or not
and perform the corresponding image segmentation. However,
the detailed annotation of cancer cells is often an ambiguous
and challenging task. In this paper, we propose
a new learning method, multiple clustered instance learning
(MCIL), to classify, segment and cluster cancer cells in
colon histopathology images. The proposed MCIL method
simultaneously performs image-level classification (cancer
vs. non-cancer image), pixel-level segmentation (cancer vs.
non-cancer tissue), and patch-level clustering (cancer subclasses).
We embed the clustering concept into the multiple
instance learning (MIL) setting and derive a principled
solution to perform the above three tasks in an integrated
framework. Experimental results demonstrate the efficiency
and effectiveness of MCIL in analyzing colon cancers.
In Computer Vision and Pattern Recognition (CVPR)