@inproceedings{91796cc14307415b98186f94edbbddca,
title = "Label-free, non-invasive multispectral imaging of cellular autofluorescence for melanoma detection",
abstract = "Melanoma, the most invasive skin cancer, has high mortality rates for advanced cases despite treatment advancements. This study explores the potential of multispectral autofluorescence imaging as a diagnostic tool for melanoma. We assessed autofluorescent signatures from cultured immortalized melanoma cell lines and fibroblasts, using feature analysis to evaluate image data. The analysis revealed highly distinctive cell groups based on autofluorescence characteristics. Additionally, we investigated the role of autofluorescence intensity features in simulating real-world conditions by evaluating unprocessed, background-corrupted data. Our findings demonstrate the efficacy of this labelfree imaging approach for melanoma detection, highlighting its promising clinical potential.",
keywords = "autofluorescence, feature analysis, fibroblasts, label-free, machine learning, melanoma, multispectral",
author = "Aline Knab and Anwer, \{Ayad G.\} and Bernadette Pedersen and Shannon Handley and Marupally, \{Abhilash Goud\} and Abbas Habibalahi and Goldys, \{Ewa M.\}",
year = "2025",
doi = "10.1117/12.3041531",
language = "English",
isbn = "9781510683327",
series = "Progress in Biomedical Optics and Imaging",
publisher = "SPIE",
pages = "1--5",
editor = "Haishan Zeng and Milind Rajadhyaksha",
booktitle = "Photonics in Dermatology and Plastic Surgery 2025",
address = "United States",
note = "Photonics in Dermatology and Plastic Surgery 2025 ; Conference date: 25-01-2025 Through 27-01-2025",
}