Deep learning-enabled, non-invasive virtual histology of skin using reflectance confocal microscopy (Conference Presentation)

Jingxi Li; Jason Garfinkel; Xiaoran Zhang; Di Wu; Yijie Zhang; Kevin de Haan; Hongda Wang; Tairan Liu; Bijie Bai; Yair Rivenson; Gennady Rubinstein; Philip O. Scumpia; Aydogan Ozcan

doi:10.1117/12.2632602

4 October 2022 Deep learning-enabled, non-invasive virtual histology of skin using reflectance confocal microscopy (Conference Presentation)

Jingxi Li, Jason Garfinkel, Xiaoran Zhang, Di Wu, Yijie Zhang, Kevin de Haan, Hongda Wang, Tairan Liu, Bijie Bai, Yair Rivenson, Gennady Rubinstein, Philip O. Scumpia, Aydogan Ozcan

Author Affiliations +

Proceedings Volume PC12204, Emerging Topics in Artificial Intelligence (ETAI) 2022; PC122040N (2022) https://doi.org/10.1117/12.2632602
Event: SPIE Nanoscience + Engineering, 2022, San Diego, California, United States

Abstract

Reflectance confocal microscopy (RCM) can provide in vivo images of the skin with cellular-level resolution; however, RCM images are grayscale, lack nuclear features and have a low correlation with histology. We present a deep learning-based virtual staining method to perform non-invasive virtual histology of the skin based on in vivo, label-free RCM images. This virtual histology framework revealed successful inference for various skin conditions, such as basal cell carcinoma, also covering distinct skin layers, including epidermis and dermal-epidermal junction. This method can pave the way for faster and more accurate diagnosis of malignant skin neoplasms while reducing unnecessary biopsies.

Conference Presentation

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Jingxi Li, Jason Garfinkel, Xiaoran Zhang, Di Wu, Yijie Zhang, Kevin de Haan, Hongda Wang, Tairan Liu, Bijie Bai, Yair Rivenson, Gennady Rubinstein, Philip O. Scumpia, and Aydogan Ozcan "Deep learning-enabled, non-invasive virtual histology of skin using reflectance confocal microscopy (Conference Presentation)", Proc. SPIE PC12204, Emerging Topics in Artificial Intelligence (ETAI) 2022, PC122040N (4 October 2022); https://doi.org/10.1117/12.2632602

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KEYWORDS

Skin

Confocal microscopy

Reflectivity

In vivo imaging

Biopsy

Tissues

Image analysis

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