Multiple Clustered Instance Learning for Histopathology Cancer Image Classification, Segmentation and Clustering

Yan Xu, Jun-Yan Zhu, Eric Chang, and Zhuowen Tu

Abstract

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.

Details

Publication typeInproceedings
Published inComputer Vision and Pattern Recognition (CVPR)
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