Constrained Deep Weak Supervision for Histopathology Image Segmentation

@article{Jia2017ConstrainedDW,
  title={Constrained Deep Weak Supervision for Histopathology Image Segmentation},
  author={Zhipeng Jia and Xingyi Huang and Eric I-Chao Chang and Yan Xu},
  journal={IEEE Transactions on Medical Imaging},
  year={2017},
  volume={36},
  pages={2376-2388}
}
In this paper, we develop a new weakly supervised learning algorithm to learn to segment cancerous regions in histopathology images. [] Key Method The contributions of our algorithm are threefold: 1) we build an end-to-end learning system that segments cancerous regions with fully convolutional networks (FCNs) in which image-to-image weakly-supervised learning is performed; 2) we develop a DWS formulation to exploit multi-scale learning under weak supervision within FCNs; and 3) constraints about positive…
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178 Citations
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178 Citations

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Strategies to Reduce the Expert Supervision Required for Deep Learning-Based Segmentation of Histopathological Images

This review identifies and describes four different approaches: the use of immunohistochemical markers as labels, realistic data augmentation, Generative Adversarial Networks (GAN), and transfer learning, which could help provide the very large number of annotated images needed to automate the segmentation of histological images using deep learning.

HistoSegResT: A Weakly Supervised Learning Method for Histopathology Image Segmentation

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Proportion constrained weakly supervised histopathology image classification

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