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