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Alzheimer's disease (AD) is a neurodegenerative disease leading to irreversible and progressive brain damage. Close monitoring is essential for slowing down the progression of AD. Magnetic Resonance Imaging (MRI) has been widely used for AD diagnosis and disease monitoring. Previous studies usually focused on extracting features from whole image or specific slices separately, but ignore the characteristics of each slice from multiple perspectives and the complementarity between features at different scales. In this study, we proposed a novel classification method based on the fusion of multi-view 2D and 3D convolutions for MRI-based AD diagnosis. Specifically, we first use multiple sub-networks to extract the local slice-level feature of each slice in different dimensions. Then a 3D convolution network was used to extract the global subject-level information of MRI. Finally, local and global information were fused to acquire more discriminative features. Experiments conducted on the ADNI-1 and ADNI-2 dataset demonstrated the superiority of this proposed model over other state-of-the-art methods for their ability to discriminate AD and Normal Controls (NC). Our model achieves 90.2% and 85.2% of accuracy on ADNI-2 and ADNI-1 respectively, thus it can be effective in AD diagnosis. The source code of our model is freely available at https://github.com/fengduqianhe/ADMultiView.

Pedestrian Attribute Recognition (PAR) has attracted increasing attention since it could provide important structural information of pedestrians for Smart Video Analysis. However, the pedestrian images are taken from a far distance significantly increase the difficulty of PAR for fine-grained attributes. To address these problems, and further improve the effects of PAR, we proposed a Multi-Scale Spatial Calibration (MSSC) module. More specifically, the module includes two submodules: first, a Spatial Calibrated Module (SCM) is proposed to extract more discriminative features of inconspicuous attributes from its surrounding regions by gathering the contextual information across different receptive fields. Moreover, in order to build the long-range dependencies of pyramid feature maps in different spatial scales, we also propose Multi-Scale Feature Fusion (MSFF) to integrate the multiple branches of low-level detailed features and high-level semantics features by non-local attention mechanism. Extensive experiments show that our proposed model could achieve state-of-the-art results on three pedestrian attribute datasets, including RAPv1, PA-100K, and RAPv2. Especially, the proposed model significantly improves the recognition effects of fine-grained attributes in low-resolution images in terms of mean Accuracy (mA) and recall. Code is available at https://github.com/iceicei/MSSC.

Background and objective Alzheimer’s Disease (AD) is a chronic and fatal neurodegenerative disease with progressive impairment of memory. Brain structural magnetic resonance imaging (sMRI) has been widely applied as important biomarkers of AD. Various machine learning approaches, especially deep learning-based models, have been proposed for the early diagnosis of AD and monitoring the disease progression on sMRI data. However, the requirement for a large number of training images still hinders the extensive usage of AD diagnosis. In addition, due to the similarities in human whole-brain structure, finding the subtle brain changes is essential to extract discriminative features from limited sMRI data effectively. Methods In this work, we proposed two types of contrastive losses with paired sMRIs to promote the diagnostic performance using group categories (G-CAT) and varying subject mini-mental state examination (S-MMSE) information, respectively. Specifically, G-CAT contrastive loss layer was used to learn the closer feature representation from sMRIs with the same categories, while ranking information from S-MMSE assists the model to explore subtle changes between individuals. Results The model was trained on ADNI-1. Comparison with baseline methods was performed on MIRIAD and ADNI-2. For the classification task on MIRIAD, S-MMSE achieves 93.5% of accuracy, 96.6% of sensitivity, and 94.9% of specificity, respectively. G-CAT and S-MMSE both reach remarkable performance in terms of classification sensitivity and specificity respectively. Comparing with state-of-the-art methods, we found this proposed method could achieve comparable results with other approaches. Conclusion The proposed model could extract discriminative features under whole-brain similarity. Extensive experiments also support the accuracy of this model, i.e., it provides better ability to identify uncertain samples, especially for the classification task of subjects with MMSE in 22–27. Source code is freely available at https://github.com/fengduqianhe/ADComparative .