Advancing dermoscopy through a synthetic hair benchmark dataset and deep learning-based hair removal.

Journal: Journal of biomedical optics

PMID: 39564076

Abstract

SIGNIFICANCE: Early detection of melanoma is crucial for improving patient outcomes, and dermoscopy is a critical tool for this purpose. However, hair presence in dermoscopic images can obscure important features, complicating the diagnostic process. Enhancing image clarity by removing hair without compromising lesion integrity can significantly aid dermatologists in accurate melanoma detection.

Authors

Lennart Jütte

Leibniz University Hannover, Hannover Centre for Optical Technologies, Hannover, Germany.
Harshkumar Patel

Leibniz University Hannover, Hannover Centre for Optical Technologies, Hannover, Germany.
Bernhard Roth

Hannover Centre for Optical Technologies, Leibniz University of Hannover, 30167 Hannover, Germany.

Keywords

Deep Learning Dermoscopy Hair Hair Removal Humans Image Interpretation, Computer-Assisted Image Processing, Computer-Assisted Melanoma Neural Networks, Computer Skin Neoplasms

External Resources

View on PubMed Access via DOI PubMed (39564076)

Advancing dermoscopy through a synthetic hair benchmark dataset and deep learning-based hair removal.

Abstract

Authors

Keywords

External Resources

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