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Title: Attention-based Skin Cancer Classification Through Hyperspectral Imaging
Authors: La Salvia, Marco
Torti, Emanuele
Gazzoni, Marco
Marenzi, Elisa
León Martín, Sonia Raquel 
Ortega Sarmiento,Samuel 
Fabelo Gómez, Himar Antonio 
Marrero Callicó, Gustavo Iván 
Leporati, Francesco
UNESCO Clasification: Investigación
Keywords: Deep learning
Medical hyperspectral imaging
Skin cancer
Vision Transformers
Issue Date: 2022
Publisher: Institute of Electrical and Electronics Engineers Inc.
Conference: 25th Euromicro Conference on Digital System Design (DSD 2022) 
Abstract: In recent years, hyperspectral imaging has been employed in several medical applications, targeting automatic diagnosis of different diseases. These images showed good performance in identifying different types of cancers. Among the methods used for classification, machine learning and deep learning techniques emerged as the most suitable algorithms to handle these data. In this paper, we propose a novel hyperspectral image classification architecture exploiting Vision Transformers. We validated the method on a real hyperspectral dataset containing 76 skin cancer images. Obtained results clearly highlight that the Vision Transforms are a suitable architecture for this task. Measured results outperform the state-of-the-art both in terms of false negative rates and of processing times. Finally, the attention mechanism is evaluated for the first time on medical hyperspectral images.
ISBN: 9781665474047
DOI: 10.1109/DSD57027.2022.00122
Source: Proceedings - 2022 25th Euromicro Conference on Digital System Design, DSD 2022 / Himar Fabelo, Samuel Ortega, Amund Skavhaug (Eds.), p. 871-876
Appears in Collections:Actas de congresos
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