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Abstract：
Hyperspectral imaging has demonstrated its potential to provide correlated spatial and spectral information of a sample by a non-contact and non-invasive technology. In the medical field, especially in histopathology, HSI has been applied for the classification and identification of diseased tissue and for the characterization of its morphological properties. In this work, we propose a hybrid scheme to classify non-tumor and tumor histological brain samples by hyperspectral imaging. The proposed approach is based on the identification of characteristic components in a hyperspectral image by linear unmixing, as a features engineering step, and the subsequent classification by a deep learning approach. For this last step, an ensemble of deep neural networks is evaluated by a cross-validation scheme on an augmented dataset and a transfer learning scheme. The proposed method can classify histological brain samples with an average accuracy of 88%, and reduced variability, computational cost, and inference times, which presents an advantage over methods in the state-of-the-art. Hence, the work demonstrates the potential of hybrid classification methodologies to achieve robust and reliable results by combining linear unmixing for features extraction and deep learning for classification.
摘要：
高光谱成像已经证明了其通过非接触和非侵入性技术提供样本的相关空间和光谱信息的潜力。在医学领域,尤其是在组织病理学方面，HSI已用于病变组织的分类和鉴定以及其形态特性的表征。在这项工作中,我们提出了一种混合方案，通过高光谱成像对非肿瘤和肿瘤组织学脑样本进行分类。所提出的方法基于通过线性解混识别高光谱图像中的特征成分，作为一个特征工程步骤，并通过深度学习方法进行后续分类。这最后一步，通过增强数据集上的交叉验证方案和迁移学习方案来评估深度神经网络的集合。所提出的方法可以对组织学脑样本进行分类，平均准确率为88%，减少可变性，计算成本，和推理时间，这与最先进的方法相比具有优势。因此，这项工作证明了混合分类方法通过结合用于特征提取的线性分解和用于分类的深度学习来实现稳健和可靠的结果的潜力。
