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To better address the recognition of abnormalities among mammographic images, in this study we apply the deep fusion learning approach based on Pre-trained models to discover the discriminative patterns between Normal and Tumor categories. We designed a deep fusion learning framework for mammographic image classification. This framework works in two main steps. After obtaining the regions of interest (ROIs) from original dataset, the first step is to train our proposed deep fusion models on those ROI patches which are randomly collected from all ROIs. We proposed the deep fusion model (Model1) to directly fuse the deep features to classify the Normal and Tumor ROI patches. To explore the association among channels of the same block, we propose another deep fusion model (Model2) to integrate the cross-channel deep features using 1 × 1 convolution. The second step is to obtain the final prediction by performing the majority voting on all patches' prediction of one ROI. The experimental results show that Model1 achieves the whole accuracy of 0.8906, recall rate of 0.913, and precision rate of 0.8077 for Tumor class. Accordingly, Model2 achieves the whole accuracy of 0.875, recall rate of 0.9565, and precision rate 0.7,586 for Tumor class. Finally, we open source our Python code at https://github.com/yxchspring/MIAS in order to share our tool with the research community.

Convolutional neural networks (CNNs) are the go-to model for hyperspectral image (HSI) classification because of the excellent locally contextual modeling ability that is beneficial to spatial and spectral feature extraction. However, CNNs with a limited receptive field pose challenges for modeling long-range dependencies. To solve this issue, we introduce a novel classification framework which regards the input HSI as a sequence data and is constructed exclusively with multilayer perceptrons (MLPs). Specifically, we propose a spectral-spatial MLP (SS-MLP) architecture, which uses matrix transposition and MLPs to achieve both spectral and spatial perception in global receptive field, capturing long-range dependencies and extracting more discriminative spectral-spatial features. Four benchmark HSI datasets are used to evaluate the classification performance of the proposed SS-MLP. Experimental results show that our pure MLP-based architecture outperforms other state-of-the-art convolution-based models in terms of both classification performance and computational time. When comparing with the SSSERN model, the average accuracy improvement of our approach is as high as 3.03%. We believe that our impressive experimental results will foster additional research on simple yet effective MLP-based architecture for HSI classification.

Convolutional neural networks (CNNs) have achieved great results in hyperspectral image (HSI) classification in recent years. However, convolution kernels are reused among different spatial locations, known as spatial-agnostic or weight-sharing kernels. Furthermore, the preference of spatial compactness in convolution (typically, 3×3 kernel size) constrains the receptive field and the ability to capture long-range spatial interactions. To mitigate the above two issues, in this article, we combine a novel operation called involution with residual learning and develop a new deep residual involution network (DRIN) for HSI classification. The proposed DRIN could model long-range spatial interactions well by adopting enlarged involution kernels and realize feature learning in a fairly lightweight manner. Moreover, the vast and dynamic involution kernels are distinct over different spatial positions, which could prioritize the informative visual patterns in the spatial domain according to the spectral information of the target pixel. The proposed DRIN achieves better classification results when compared with both traditional machine learning-based and convolution-based methods on four HSI datasets. Especially in comparison with the convolutional baseline model, i.e., deep residual network (DRN), our involution-powered DRIN model increases the overall classification accuracy by 0.5%, 1.3%, 0.4%, and 2.3% on the University of Pavia, the University of Houston, the Salinas Valley, and the recently released HyRANK HSI benchmark datasets, respectively, demonstrating the potential of involution for HSI classification.

The convolutional neural network (CNN) can automatically extract hierarchical feature representations from raw data and has recently achieved great success in the classification of hyperspectral images (HSIs). However, most CNN based methods used in HSI classification neglect adequately utilizing the strong complementary yet correlated information from each convolutional layer and only employ the last convolutional layer features for classification. In this paper, we propose a novel fully dense multiscale fusion network (FDMFN) that takes full advantage of the hierarchical features from all the convolutional layers for HSI classification. In the proposed network, shortcut connections are introduced between any two layers in a feed-forward manner, enabling features learned by each layer to be accessed by all subsequent layers. This fully dense connectivity pattern achieves comprehensive feature reuse and enforces discriminative feature learning. In addition, various spectral-spatial features with multiple scales from all convolutional layers are fused to extract more discriminative features for HSI classification. Experimental results on three widely used hyperspectral scenes demonstrate that the proposed FDMFN can achieve better classification performance in comparison with several state-of-the-art approaches.

To learn discriminative features, hyperspectral image (HSI), containing 3-D cube data, is a preferable means of capturing multi-head self-attention from both spatial and spectral domains if the burden in model optimization and computation is low. In this paper, we design a dual multi-head contextual self-attention (DMuCA) network for HSI classification with the fewest possible parameters and lower computation costs. To effectively capture rich contextual dependencies from both domains, we decouple the spatial and spectral contextual attention into two sub-blocks, SaMCA and SeMCA, where depth-wise convolution is employed to contextualize the input keys in the pure dimension. Thereafter, multi-head local attentions are implemented as group processing when the keys are alternately concatenated with the queries. In particular, in the SeMCA block, we group the spatial pixels by evenly sampling and create multi-head channel attention on each sampling set, to reduce the number of the training parameters and avoid the storage increase. In addition, the static contextual keys are fused with the dynamic attentional features in each block to strengthen the capacity of the model in data representation. Finally, the decoupled sub-blocks are weighted and summed together for 3-D attention perception of HSI. The DMuCA module is then plugged into a ResNet to perform HSI classification. Extensive experiments demonstrate that our proposed DMuCA achieves excellent results over several state-of-the-art attention mechanisms with the same backbone.

Supported Pt-based catalysts have been identified as highly selective catalysts for CO oxidation, but their potential for applications has been hampered by the high cost and scarcity of Pt metals as well as aggregation problems at relatively high temperatures. In this work, nanorod structured (TiO2−Pt)/CeO2 catalysts with the addition of 0.3 at% Pt and different atomic ratios of Ti were prepared through a combined dealloying and calcination method. XRD, XPS, SEM, TEM, and STEM measurements were used to confirm the phase composition, surface morphology, and structure of synthesized samples. After calcination treatment, Pt nanoparticles were semi-inlayed on the surface of the CeO2 nanorod, and TiO2 was highly dispersed into the catalyst system, resulting in the formation of (TiO2−Pt)/CeO2 with high specific surface area and large pore volume. The unique structure can provide more reaction path and active sites for catalytic CO oxidation, thus contributing to the generation of catalysts with high catalytic activity. The outstanding catalytic performance is ascribed to the stable structure and proper TiO2 doping as well as the combined effect of Pt, TiO2, and CeO2. The research results are of importance for further development of high catalytic performance nanoporous catalytic materials.

To train a convolutional neural network (CNN) from scratch is not suitable for medical image tasks with insufficient data. Benefiting from the transfer learning, the pre-trained CNN model can provide a reliable initial solution for model optimization of medical image classification. A key concern in breast cancer histology classification is that the model should cover the multi-scale features including nuclei-scale, nuclei organization, and structure-scale features. Inspired by these conjectures, we proposed a novel fusion convolutional neural network (FCNN) based on pre-trained VGG19. The FCNN fuses the shallow, intermediate abstract, and abstract layers to approximately cover the multi-scale features. In order to improve the sensitivity of carcinoma classes, the prediction priority is introduced to enable the lesions can be detected as early as possible. Experimental results show that the proposed FCNN can approximately cover the nuclei-scale, nuclei organization, and structure-scale features. Accuracies of 85%, 75%, and 80.56% are achieved in Initial, Extended, and Overall test set, respectively. The source code for this research is available at https://github.com/yxchspring/breasthistolgoy .

Filter banks transferred from a pre-trained deep convolutional network exhibit significant performance in heightening the inter-class separability for hyperspectral image feature extraction, but weakening the intra-class consistency simultaneously. In this paper, we propose a new superpixel-based relational auto-encoder for cohesive spectral–spatial feature learning. Firstly, multiscale local spatial information and global semantic features of hyperspectral images are extracted by filter banks transferred from the pre-trained VGG-16. Meanwhile, we utilize superpixel segmentation to construct the low-dimensional manifold embedded in the spectral domain. Then, representational consistency constraint among each superpixel is added in the objective function of sparse auto-encoder, which iteratively assist and supervisedly learn hidden representation of deep spatial feature with greater cohesiveness. Superpixel-based local consistency constraint in this work not only reduces the computational complexity, but builds the neighborhood relationships adaptively. The final feature extraction is accomplished by collaborative encoder of spectral–spatial feature and weighting fusion of multiscale features. A large number of experimental results demonstrate that our proposed method achieves expected results in discriminant feature extraction and has certain advantages over some existing methods, especially on extremely limited sample conditions.

Convolutional neural networks (CNNs) have recently shown outstanding capability for hyperspectral image (HSI) classification. In this work, a novel CNN model is proposed, which is wider than other existing deep learning-based HSI classification models. Based on the fact that very deep residual networks (ResNets) behave like ensembles of relatively shallow networks, our proposed network, called multipath ResNet (MPRN), employs multiple residual functions in the residual blocks to make the network wider, rather than deeper. The proposed network consists of shorter-medium paths for efficient gradient flow and replaces the stacking of multiple residual blocks in ResNet with fewer residual blocks but more parallel residual functions in each of it. Experimental results on three real hyperspectral data sets demonstrate the superiority of the proposed method over several state-of-the-art classification methods.

Following publication, concerns were raised regarding the peer-review process related to the publication of this article [...].Following publication, concerns were raised regarding the peer-review process related to the publication of this article [...].