Corpus ID: 204401822

Deep Learning for Prostate Pathology

@article{Eminaga2019DeepLF,
  title={Deep Learning for Prostate Pathology},
  author={O. Eminaga and Y. Tolkach and C. Kunder and M. Abbas and Ryan Han and R. Nolley and A. Semjonow and M. Boegemann and S. Huss and A. Loening and R. West and G. Sonn and Richard E. Fan and O. Bettendorf and J. Brooks and D. Rubin},
  journal={ArXiv},
  year={2019},
  volume={abs/1910.04918}
}
  • O. Eminaga, Y. Tolkach, +13 authors D. Rubin
  • Published 2019
  • Medicine, Computer Science, Biology, Engineering
  • ArXiv
  • The current study detects different morphologies related to prostate pathology using deep learning models; these models were evaluated on 2,121 hematoxylin and eosin (H&E) stain histology images captured using bright field microscopy, which spanned a variety of image qualities, origins (whole slide, tissue micro array, whole mount, Internet), scanning machines, timestamps, H&E staining protocols, and institutions. For case usage, these models were applied for the annotation tasks in clinician… CONTINUE READING

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