Weakly Supervised Learning using Attention gates for colon cancer histopathological image segmentation

作者：

Highlights：

• A novel training strategy is introduced for sparsely annotated histopathological data.

• Both UNet and Att-UNet are used for colon cancer Whole Slide image segmentation.

• Enhanced lighter Att-UNet models are introduced for Whole Slide Image segmentation.

摘要

•A novel training strategy is introduced for sparsely annotated histopathological data.•Both UNet and Att-UNet are used for colon cancer Whole Slide image segmentation.•Enhanced lighter Att-UNet models are introduced for Whole Slide Image segmentation.

论文关键词：Digital pathology,Colon cancer,Weak supervision,Attention gates,Deep Learning,Image segmentation

论文评审过程：Received 16 February 2022, Revised 7 September 2022, Accepted 15 September 2022, Available online 24 September 2022, Version of Record 3 October 2022.

论文官网地址：https://doi.org/10.1016/j.artmed.2022.102407