@article{10902/15658,
year = {2018},
month = {12},
url = {http://hdl.handle.net/10902/15658},
abstract = {Early detection and diagnosis is a must in secondary prevention of melanoma and other cancerous lesions of the skin. In this work, we present an online, reservoir-based, non-parametric estimation and classification model that allows for this functionality on pigmented lesions, such that detection thresholding can be tuned to maximize accuracy and/or minimize overall false negative rates. This system has been tested in a dataset consisting of 116 patients and a total of 124 hyperspectral images of nevi, raised nevi and melanomas, detecting up to 100% of the suspicious lesions at the expense of some false positives.},
organization = {MINECO (Ministerio de Economía y Competitividad), Instituto de Salud Carlos III (ISCIII) (DTS15/00238, DTS17/00055, TEC2016-76021-C2-2-R); CIBER-BBN; IDIVAL (INNVAL 16/02); MECD (Ministerio de Educación, Cultura y Deporte) (FPU16/05705).},
publisher = {The Optical Society},
publisher = {Biomedical Optics Express, 2018, 9(12), 6283-6301},
title = {On the spectral signature of melanoma: a non-parametric classification framework for cancer detection in hyperspectral imaging of melanocytic lesions},
author = {Pardo Franco, Arturo and Gutiérrez Gutiérrez, José Alberto and Lihacova, Ilze and López Higuera, José Miguel and Conde Portilla, Olga María},
}