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{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T04:44:43Z","timestamp":1726029883776},"reference-count":38,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2022,8,17]],"date-time":"2022-08-17T00:00:00Z","timestamp":1660694400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007757","name":"Agencia Canaria de Investigaci\u00f3n, Innovaci\u00f3n y Sociedad de la Informaci\u00f3n","doi-asserted-by":"publisher","award":["POC 2014- 2020 and FJC2020-043474-I"],"id":[{"id":"10.13039\/501100007757","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Spanish Government","award":["PID2020-116417RB-C42"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"In recent years, researchers designed several artificial intelligence solutions for healthcare applications, which usually evolved into functional solutions for clinical practice. Furthermore, deep learning (DL) methods are well-suited to process the broad amounts of data acquired by wearable devices, smartphones, and other sensors employed in different medical domains. Conceived to serve the role of diagnostic tool and surgical guidance, hyperspectral images emerged as a non-contact, non-ionizing, and label-free technology. However, the lack of large datasets to efficiently train the models limits DL applications in the medical field. Hence, its usage with hyperspectral images is still at an early stage. We propose a deep convolutional generative adversarial network to generate synthetic hyperspectral images of epidermal lesions, targeting skin cancer diagnosis, and overcome small-sized datasets challenges to train DL architectures. Experimental results show the effectiveness of the proposed framework, capable of generating synthetic data to train DL classifiers.<\/jats:p>","DOI":"10.3390\/s22166145","type":"journal-article","created":{"date-parts":[[2022,8,18]],"date-time":"2022-08-18T02:53:30Z","timestamp":1660791210000},"page":"6145","source":"Crossref","is-referenced-by-count":15,"title":["Deep Convolutional Generative Adversarial Networks to Enhance Artificial Intelligence in Healthcare: A Skin Cancer Application"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"http:\/\/orcid.org\/0000-0003-3724-8213","authenticated-orcid":false,"given":"Marco","family":"La Salvia","sequence":"first","affiliation":[{"name":"Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-8437-8227","authenticated-orcid":false,"given":"Emanuele","family":"Torti","sequence":"additional","affiliation":[{"name":"Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-4287-3200","authenticated-orcid":false,"given":"Raquel","family":"Leon","sequence":"additional","affiliation":[{"name":"Research Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), 35001 Las Palmas de Gran Canaria, Spain"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-9794-490X","authenticated-orcid":false,"given":"Himar","family":"Fabelo","sequence":"additional","affiliation":[{"name":"Research Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), 35001 Las Palmas de Gran Canaria, Spain"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-7519-954X","authenticated-orcid":false,"given":"Samuel","family":"Ortega","sequence":"additional","affiliation":[{"name":"Research Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), 35001 Las Palmas de Gran Canaria, Spain"},{"name":"Norwegian Institute of Food, Fisheries and Aquaculture Research (Nofima), 6122 Troms\u00f8, Norway"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-7835-9660","authenticated-orcid":false,"given":"Beatriz","family":"Martinez-Vega","sequence":"additional","affiliation":[{"name":"Research Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), 35001 Las Palmas de Gran Canaria, Spain"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-3784-5504","authenticated-orcid":false,"given":"Gustavo M.","family":"Callico","sequence":"additional","affiliation":[{"name":"Research Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), 35001 Las Palmas de Gran Canaria, Spain"}]},{"given":"Francesco","family":"Leporati","sequence":"additional","affiliation":[{"name":"Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.glohj.2020.04.002","article-title":"A Review of Medical Artificial Intelligence","volume":"4","author":"Liu","year":"2020","journal-title":"Glob. 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