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Follow on Google News | Deep learning virtual staining of tissue facilitates rapid assessment of breast cancer biomarkerBy: UCLA ITA A UCLA research team developed a computational staining approach powered by deep learning, which performs the HER2 staining without requiring any chemicals. The research team captured the autofluorescence information of the unstained breast tissue, which is naturally emitted by biological structures when they absorb light. They further trained a deep neural network that rapidly transforms these stain-free autofluorescence images into virtual histological images, revealing the accurate color and contrast as if the tissue sections were chemically stained for HER2. This computational staining process takes only a few minutes per sample and does not need expensive facilities or toxic chemicals. Using only a computer, the HER2 staining could be accomplished much faster and cost-effectively, accelerating breast cancer assessments and treatment. Board-certified pathologists blindly validated this AI-based virtual HER2 staining technique in terms of both its diagnostic value and stain quality. The pathologists confirmed that the deep learning-generated images provide the equivalent diagnostic accuracy for HER2 assessment and have a staining quality comparable to the standard images chemically stained in the laboratory. This deep learning-powered virtual HER2 staining approach eliminates the need for costly, laborious, and time-consuming HER2 staining procedures performed by histology experts and could be extended to staining of other cancer-related biomarkers to accelerate the traditional histopathology and diagnostic workflow in clinical settings. This research was led by Dr. Aydogan Ozcan, Chancellor's Professor and Volgenau Chair for Engineering Innovation at UCLA Electrical and Computer Engineering and Bioengineering. The UCLA team collaborated with Dr. Morgan Angus Darrow, Dr. Elham Kamangar, and Dr. Han Sung Lee, breast pathologists at the Department of Pathology and Laboratory Medicine from UC Davis. The other authors of this work include Bijie Bai, Hongda Wang, Yuzhu Li, Kevin de Haan, Francesco Colonnese, Yujie Wan, Jingyi Zuo, Ngan B. Doan, Xiaoran Zhang, Yijie Zhang, Jingxi Li, Xilin Yang, Wenjie Dong and Dr. Yair Rivenson, all affiliated with UCLA. NSF Biophotonics Program and NIH/National Center supported the research. See the article: https://spj.sciencemag.org/ End
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