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Xue Y, Ye J, Zhou Q, et al. Selective synthetic augmentation with HistoGAN for improved histopathology image classification. Med Image Anal. 2020;67:101816doi: 10.1016/j.media.2020.101816.
Xue, Y., Ye, J., Zhou, Q., Long, L. R., Antani, S., Xue, Z., Cornwell, C., Zaino, R., Cheng, K. C., & Huang, X. (2021). Selective synthetic augmentation with HistoGAN for improved histopathology image classification. Medical image analysis, 67101816. https://doi.org/10.1016/j.media.2020.101816
Xue, Yuan, et al. "Selective synthetic augmentation with HistoGAN for improved histopathology image classification." Medical image analysis vol. 67 (2021): 101816. doi: https://doi.org/10.1016/j.media.2020.101816
Xue Y, Ye J, Zhou Q, Long LR, Antani S, Xue Z, Cornwell C, Zaino R, Cheng KC, Huang X. Selective synthetic augmentation with HistoGAN for improved histopathology image classification. Med Image Anal. 2021 Jan;67:101816. doi: 10.1016/j.media.2020.101816. Epub 2020 Oct 01. PMID: 33080509; PMCID: PMC8647936.
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Med Image Anal. 2021 Jan;67:101816. doi: 10.1016/j.media.2020.101816. Epub 2020 Oct 01.

Selective synthetic augmentation with HistoGAN for improved histopathology image classification.

Medical image analysis

Yuan Xue, Jiarong Ye, Qianying Zhou, L Rodney Long, Sameer Antani, Zhiyun Xue, Carl Cornwell, Richard Zaino, Keith C Cheng, Xiaolei Huang

Affiliations

  1. College of Information Sciences and Technology, The Pennsylvania State University, University Park, PA 16802, USA.
  2. Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, MD 20892, USA.
  3. Department of Pathology, Penn State Health Milton S. Hershey Medical Center and Penn State College Of Medicine, Hershey, PA 17033, USA.
  4. College of Information Sciences and Technology, The Pennsylvania State University, University Park, PA 16802, USA. Electronic address: [email protected].

PMID: 33080509 PMCID: PMC8647936 DOI: 10.1016/j.media.2020.101816

Abstract

Histopathological analysis is the present gold standard for precancerous lesion diagnosis. The goal of automated histopathological classification from digital images requires supervised training, which requires a large number of expert annotations that can be expensive and time-consuming to collect. Meanwhile, accurate classification of image patches cropped from whole-slide images is essential for standard sliding window based histopathology slide classification methods. To mitigate these issues, we propose a carefully designed conditional GAN model, namely HistoGAN, for synthesizing realistic histopathology image patches conditioned on class labels. We also investigate a novel synthetic augmentation framework that selectively adds new synthetic image patches generated by our proposed HistoGAN, rather than expanding directly the training set with synthetic images. By selecting synthetic images based on the confidence of their assigned labels and their feature similarity to real labeled images, our framework provides quality assurance to synthetic augmentation. Our models are evaluated on two datasets: a cervical histopathology image dataset with limited annotations, and another dataset of lymph node histopathology images with metastatic cancer. Here, we show that leveraging HistoGAN generated images with selective augmentation results in significant and consistent improvements of classification performance (6.7% and 2.8% higher accuracy, respectively) for cervical histopathology and metastatic cancer datasets.

Copyright © 2020. Published by Elsevier B.V.

Keywords: Histopathology image classification; Medical image synthesis; Synthetic data augmentation

Conflict of interest statement

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this pa

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