Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy.
Research Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), 35001 Las Palmas de Gran Canaria, Spain.
Sensors (Basel). 2022 Aug 17;22(16):6145. doi: 10.3390/s22166145.
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.
近年来，研究人员为医疗保健应用设计了几种人工智能解决方案，这些方案通常演变成临床实践的功能解决方案。此外，深度学习 (DL) 方法非常适合处理可穿戴设备、智能手机和其他在不同医疗领域使用的传感器获取的大量数据。高光谱图像作为一种非接触式、非电离和无标记的技术而被设想用于充当诊断工具和手术指导。然而，缺乏用于有效训练模型的大型数据集限制了深度学习在医学领域的应用。因此，它在高光谱图像中的使用仍处于早期阶段。我们提出了一种深度卷积生成对抗网络，以生成针对皮肤癌诊断的表皮病变的合成高光谱图像，并克服用于训练深度学习架构的小数据集挑战。实验结果表明，所提出的框架能够生成用于训练深度学习分类器的合成数据，这是有效的。
