In this article, we designed an adaptive residual convolutional neural network (ARCNN) that takes raw hyperspectral image (HSI) cubes as input data for land-cover classification. In this network, spectral and spatial feature extraction blocks are explored to learn discriminative features from abundant spectral information and spatial contexts in HSIs. The proposed ARCNN is an end-to-end deep learning framework that alleviates the decliningaccuracy phenomenon of deep learning models, and it also ranks the correlation and importance of each band in HSIs. Furthermore, the residual blocks connect every other 3-D convolutional layer by using an identity mapping, which facilitates backpropagation of gradients. In order to address the common issue of imbalance between high dimensionality and limited availability of training samples for HSI classification, an attention mechanism and a feature fusion block are investigated to improve the performance of the ARCNN. Finally, some strategies, batch normalization and dropout, are imposed on every convolutional layer to regularize the learning process. Therefore, the ARCNN method brings benefits to extract discriminative features, and it is easier to avoid overfitting. Experimental results on three public HSI datasets demonstrate the effectiveness of the ARCNN over some state-of-the-art methods.

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Accurate histopathological analysis is the core step of early diagnosis of cholangiocarcinoma (CCA). Compared with color pathological images, hyperspectral pathological images have advantages for providing rich band information. Existing algorithms of HSI classification are dominated by convolutional neural network (CNN), which has the deficiency of distorting spectral sequence information of HSI data. Although vision transformer (ViT) alleviates this problem to a certain extent, the expressive power of transformer encoder will gradually decrease with increasing number of layers, which still degrades the classification performance. In addition, labeled HSI samples are limited in practical applications, which restricts the performance of methods. To address these issues, this paper proposed a multi-layer collaborative generative adversarial transformer termed MC-GAT for CCA classification from hyperspectral pathological images. MC-GAT consists of two pure transformer-based neural networks including a generator and a discriminator. The generator learns the implicit probability of real samples and transforms noise sequences into band sequences, which produces fake samples. These fake samples and corresponding real samples are mixed together as input to confuse the discriminator, which increases model generalization. In discriminator, a multi-layer collaborative transformer encoder is designed to integrate output features from different layers into collaborative features, which adaptively mines progressive relations from shallow to deep encoders and enhances the discriminating power of the discriminator. Experimental results on the Multidimensional Choledoch Datasets demonstrate that the proposed MC-GAT can achieve better classification results than many state-of-the-art methods. This confirms the potentiality of the proposed method in aiding pathologists in CCA histopathological analysis from hyperspectral imagery.

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