Deep learning-based grading of ductal carcinoma in situ in breast histopathology images

@article{Wetstein2021DeepLG,
  title={Deep learning-based grading of ductal carcinoma in situ in breast histopathology images},
  author={Suzanne C. Wetstein and Nikolas Stathonikos and Josien P. W. Pluim and Yujing J. Heng and Natalie D. ter Hoeve and Celien P. H. Vreuls and Paul J. van Diest and Mitko Veta},
  journal={Laboratory Investigation; a Journal of Technical Methods and Pathology},
  year={2021},
  volume={101},
  pages={525 - 533}
}
Ductal carcinoma in situ (DCIS) is a non-invasive breast cancer that can progress into invasive ductal carcinoma (IDC). Studies suggest DCIS is often overtreated since a considerable part of DCIS lesions may never progress into IDC. Lower grade lesions have a lower progression speed and risk, possibly allowing treatment de-escalation. However, studies show significant inter-observer variation in DCIS grading. Automated image analysis may provide an objective solution to address high… Expand
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