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arXiv · 1908.10555

CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation

Gang Xu·Zhigang Song·Zhuo Sun·Calvin Ku·Zhe Yang·Cancheng Liu·Shuhao Wang·Jianpeng Ma·Wei Xu
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Abstract

Histopathology image analysis plays a critical role in cancer diagnosis and treatment. To automatically segment the cancerous regions, fully supervised segmentation algorithms require labor-intensive and time-consuming labeling at the pixel level. In this research, we propose CAMEL, a weakly supervised learning framework for histopathology image segmentation using only image-level labels. Using multiple instance learning (MIL)-based label enrichment, CAMEL splits the image into latticed instances and automatically generates instance-level labels. After label enrichment, the instance-level labels are further assigned to the corresponding pixels, producing the approximate pixel-level labels and making fully supervised training of segmentation models possible. CAMEL achieves comparable performance with the fully supervised approaches in both instance-level classification and pixel-level segmentation on CAMELYON16 and a colorectal adenoma dataset. Moreover, the generality of the automatic labeling methodology may benefit future weakly supervised learning studies for histopathology image analysis.

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Gang Xu, Zhigang Song, Zhuo Sun, Calvin Ku, Zhe Yang, Cancheng Liu, Shuhao Wang, Jianpeng Ma, Wei Xu. 2019-08-28. CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation. https://arxiv.org/abs/1908.10555

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