In recent years, hyperspectral image (HSI) classification technology has received more attention. Deep learning methods have been gradually applied to the classification of HSIs. Convolutional neural networks-based model, especially the residual networks (ResNets), have shown its excellent performance. In HSI samples, there are usually some noise pixels near the center pixel, which are not conductive for the extraction of spectral-spatial features and will have a negative impact on the classification performance. In our previous study, a spectral similarity-based spatial attention module integrated with 3D ResNet was designed to highlight the effect of center pixels on spatial attention. However, during the generation of the spatial attention, the characteristics of different similarities are ignored. Meanwhile, the employment of 3D ResNet may generate a large amount of redundancy to waste the computing resources. Therefore, an improved spectral-similarity-based spatial attention module with pre-activation and a 3Dinverted residual attention network, is proposed. The pre-activation strategy is designed to follow the feature of each similarity measurement, in which the Canberra distance is invoked to simplify the computational complexity of similarity. A3Dinverted residual module is integrated with squeeze-and-excite attention modules to handle spatial and spectral-spatial features more efficiently. Experimental results on three public HSI data sets demonstrate a better classification performance of the proposal comparing with some of the state of the arts.
Neonatal hyperbilirubinemia is a disease of bilirubin metabolism disorder, which is a common in newborns. Without timely medical attention, neonatal hyperbilirubinemia may develop into acute bilirubin encephalopathy, resulting in serious long-term neurological deficits. Magnetic resonance imaging, as a non-invasive imaging technique, is widely used in the diagnosis of acute bilirubin encephalopathy in newborns. However, the T1-weighted images of magnetic resonance imaging of newborns with normal myelin development and newborns with acute bilirubin encephalopathy have similar high signal intensity, making it difficult to make a clinical diagnosis based on the conventional radiological reading. As an important computer-aided diagnosis method, deep convolutional neural network has been widely used to improve the work efficiency of radiologists. In this paper, a convolutional neural network based on classification network for acute bilirubin encephalopathy is proposed. It contains a feature fusion section and a fairly deep Resnet classification network. Experimental results show that the performance of the proposal is better than those of deep learning models in discussion.
Hyperspectral image (HSI) classification aims to assign each pixel with a proper land-cover label. Over the past few years, HSI classification using convolutional neural networks (CNNs) has progressed significantly. In spite of their effectiveness, CNN is not efficient in capturing the hierarchical structure of the entities in the images and does not fully consider the spatial information that is important to classification. Capsule network (CapsNet) preserves the hierarchy between different parts of the entity in an image by replacing scalar representations with vectors which has become an active area in the classification field in the past years. In this article, a capsule attention module-based CapsNet (CAM-CapsNet) is proposed which is not only employed to improve the performance of HSI classification but also to reduce the computation cost of the model. Specifically, 3-D convolutional layers are used to extract higher level spatial and spectral features. The local connection dynamic routing is proposed to reduce the number of parameters in the network. For the sake of boosting the representational capacity of CapsNet for spectral-spatial HSI classification, the network is improved by discriminating the significance of different spectral bands. A capsule attention module is designed to adaptively recalibrate spectral bands by selectively emphasizing informative bands and suppressing the less useful ones. The CAM-CapsNet was trained on three HSI datasets and achieved higher accuracy by comparing with some of the state-of-the-art models.
Autism spectrum disorder is a heterogeneous neurological disorder. The early diagnosis of autism is critical to apply effective treatment. Presently, most diagnoses are based on behavioral observations of symptoms. There has been an increasing number of approaches using magnetic resonance imaging with the development of deep learning in recent years. However, the interfering elements and insignificant differentiation between positive and negative samples have seriously affected the classification performance. In this paper, a multi-scale information fusion mechanism is proposed to combine with attention sub-nets to establish an end-to-end classification model, which selects appropriate fusion strategies for the outputs of different layers of the convolutional neural network to make comprehensive use of the information at different levels of the image. Experiments are conducted by using the dataset of Autistic Brain Imaging Data Exchange. The results show that the proposal achieves better performance than the models in comparison.
Studies have found autism spectrum disorder is a diffuse developmental disease of the central nervous system. The majority of autism cases result from a combination of genetic predisposition and environmental factors that influence early brain development, despite a few being caused by genes alone. Traditional diagnosis of autism spectrum disorder is usually through interviews and questionnaires, which takes plenty of time and might be misdiagnosed. The primary purpose of this study is to compare different classification methods for distinguishing autism spectrum disorder from typical development by machine learning and deep learning in recent years. The experiments are conducted to discuss their strengths and weaknesses, which, in turn, results are presented for further research.
Since the non-specificity of acute bilirubin encephalopathy (ABE), accurate classification based on structural MRI is intractable. Due to the complexity of the diagnosis, multi-modality fusion has been widely studied in recent years. The most current medical image classification researches only fuse image data of different modalities. Phenotypic features that may carry useful information are usually excluded from the model. In the paper, a multi-modal fusion strategy for classifying ABE was proposed, which combined the different modalities of MRI with clinical phenotypic data. The baseline consists of three individual paths for training different MRI modalities i.e., T1, T2, and T2-flair. The feature maps from different paths were concatenated to form multi-modality image features. The phenotypic inputs were encoded into a two-dimensional vector to prevent the loss of information. The Text-CNN was applied as the feature extractor of the clinical phenotype. The extracted text feature map will be concatenated with the multi-modality image feature map along the channel dimension. The obtained MRI-phenotypic feature map is sent to the fully connected layer. We trained/tested (80%/20%) the approach on a database containing 800 patients data. Each sample is composed of three modalities 3D brain MRI and its corresponding clinical phenotype data. Different comparative experiments were designed to explore the fusion strategy. The results demonstrate that the proposal achieves an accuracy of 0.78, a sensitivity of 0.46, and a specificity of 0.99, which outperforms the model using MRI or clinical phenotype as input alone. Our work suggests the fusion of clinical phenotype data and image data can improve the performance of ABE classification.
Are there any abnormal reflection in the structural Magnetic Resonance Imaging(sMRI) of patients with autism spectrum disorder (ASD)? Although a few brain regions have been somehow implicated in the pathophysiologic mechanism of the disorder, the gold-standard for diagnosis based on sMRI has not been reached in the academic community. Recently, the powerful deep learning algorithms have been widely studied and applied, which provides a chance to explore the brain structural abnormalities of ASD by the visualization based on the deep learning model. In this paper, a 3D-ResNet with an attention subnet for ASD classification is proposed. The model combined the residual module and the attention subnet to mask the regions which are relevant or irrelevant to the classification during the feature extraction. The model was trained and tested by sMRI from Autism Brain Imaging Data Exchange (ABIDE). The result of 5-fold cross-validation shows an accuracy of 75%. The Grad-CAM was further applied to display the emphasized composition of the model during classification. The class activation mapping of multiple slices of the representation sMRI was visualized. The results show that there are high related signals in the regions near the hippocampus, corpus callosum, thalamus, and amygdala. This result may confirm some of the previous hypotheses. The work is not only limited to the classification of ASD but also attempts to explore the anatomic abnormality with a quite promising visualization-based deep learning approach.
Makeup, derived from human`s pursuit of beauty, is widely accepted by the public. Despite its popularization, there are little effect been made to tackle the makeup face verification challenges. Aiming to promote existing verification system to accept or reject the claimed identity of a person with makeup in an image, a makeup robust face verification framework is proposed based upon a generative adversarial network. The proposal synthesizes non-makeup face images from makeup images for further verification. Specifically, a patchwise contrastive loss is introduced in the generative model to narrow the distance between makeup and non-makeup images. The challenge in the state-of-the-art is the employment of a pre-specified and hand-designed loss function to measure the performance, which is not the case in the proposal. Experimental results demonstrate that the proposal generates non-makeup faces with few artifacts and achieves 96.3% accuracy on Dataset1 in face verification, which is at least 0.8% better than some well discussed models.
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