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Style transfer strategy for developing a generalizable deep learning application in digital pathology.

Seo Jeong Shin1, Seng Chan You2, Hokyun Jeon1

  • 1Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Republic of Korea.

Computer Methods and Programs in Biomedicine
|November 7, 2020
PubMed
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This summary is machine-generated.

Image-to-image style transfer significantly improved deep learning model performance for identifying ovarian cancer malignancy on pathology slides. This technique enhances diagnostic accuracy, especially with limited local data, by harmonizing image styles.

Area of Science:

  • Digital Pathology
  • Artificial Intelligence in Medicine
  • Computer Vision

Background:

  • Deep learning models for medical image analysis face challenges due to data heterogeneity across institutions, impacting diagnostic performance.
  • Rare cancers, like ovarian cancer, often suffer from a paucity of available pathology data, further limiting model development.
  • Inter- and intra-institutional variations in tissue preparation create significant obstacles for robust AI in digital pathology.
Keywords:
CycleGANDeep LearningDigital PathologyStyle Transfer

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