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Schizophrenia (SCZ) is a devastating mental condition with significant negative consequences for patients, making correct and prompt diagnosis crucial. The purpose of this study is to use structural magnetic resonance image (MRI) to better classify individuals with SCZ from control normals (CN) and to locate a region of the brain that represents abnormalities associated with SCZ. Deep learning (DL), which is based on the nervous system, could be a very useful tool for doctors to accurately predict, diagnose, and treat SCZ. Gray Matter (GM), Cerebrospinal Fluid (CSF), and White Matter (WM) brain regions are extracted from 99 MRI images obtained from the open-source OpenNeuro database to demonstrate SCZ's regional relationship. In this paper, we use a pretrained ResNet-50 deep network to extract features from MRI images and an ensemble deep random vector functional link (edRVFL) network to classify those features. By examining the results obtained, the edRVFL deep model provides the highest classification accuracy of 96.5% with WM and is identified as the best-performing algorithm compared to the traditional algorithms. Furthermore, we examined the GM, WM, and CSF tissue volumes in CN subjects and SCZ patients using voxel-based morphometry (VBM), and the results show 1363 significant voxels, 6.90 T-value, and 6.21 Z-value in the WM region of SCZ patients. In SCZ patients, WM is most closely linked to structural alterations, as evidenced by VBM analysis and the DL model. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a degenerative neurological ailment that begins with memory loss and ultimately leads to a total loss of mental capacity. Researchers are interested in using magnetic resonance imaging (MRI) and positron emission tomography (PET) to find people with mild cognitive impairment (MCI), which is a stage before Alzheimer's disease (AD). Significant hippocampal loss and temporal lobe atrophy characterize the transition from MCI to AD, which can be visualized using T1-W structural MRI. PET visualizes brain glucose metabolism, which indicates neuronal activity, making it a viable neuroimaging method for AD diagnosis. The extraction and fusion of structural and metabolite information about brain alterations contained in multimodal data is crucial for achieving an appropriate classification result. Therefore, in this work a new end-to-end coupled-GAN (CGAN) architecture is introduced. The proposed CGANC network consists of two sub-models: a CGAN for extraction of fused features from multimodal data, and a CNN classifier to classify these features. The proposed CGAN model is trained to encode MRI and PET images into a shared latent space. The fused features are extracted from this shared latent space and then are classified according to particular stage of AD. In order to test the effectiveness of the suggested approach, experiments are done on the publicly available ADNI dataset and compared with state-of-the-art methods. The proposed method's source code will be made freely available at https://github.com/ChandrajitChoudhury/CGAN-AD. • MRI and PET images are fused to utilize structural and metabolic features for AD diagnosis. • An adversarial learning-based method is proposed to extract fused features. • A coupled adversarial subnetwork and a classification subnetwork have been designed. • Experiments are done on the publicly available ADNI dataset. [ABSTRACT FROM AUTHOR]

Availability of high-quality large annotated data is indeed a significant challenge in healthcare. In addition, privacy concerns and data-sharing restrictions often hinder access to large and diverse medical image datasets. To reduce the requirement for annotated training data, self-supervised pre-training strategies on nonannotated data have been extensively used, whereas collaborative algorithm training without the need to exchange the underlying data. In this paper, we introduce a novel federated learning-based self-supervised spatial–temporal transformer's fusion (SSFL) for cardiovascular image segmentation. The integration of spatial–temporal swin transformer is used to extract the features from 3D SAX multiple phases (full cycle of cardiac heart). An efficient self-supervised contrastive framework consisting of a spatial–temporal transformer network with 25 encoders is used to model the temporal features. The spatial and temporal features are fused and forwarded to the decoder for cardiac heart segmentation using cine MRI images. To further improve segmentation, we use an attention-based unpaired GAN model to map or transfer the style from ACDC to M&Ms and use synthetically generated volumes in the proposed self-supervised approach. Experiments with three different cardiovascular image segmentation tasks, such as segmentation of the right ventricle, left ventricle, and myocardium, showed significant improvement compared to the state-of-the-art segmentation framework. • Efficient self-supervised contrastive framework consisting of a spatial–temporal transformer's fusion. • Extracted features from multiple 3D SAX phases (full cardiac heart cycle) for 4D medical image segmentation. • We used 25 encoders to model the temporal features, and passed them to the single decoder. • Uses an attention-based unpaired GAN model to map or transfer the style from ACDC to M&Ms. • Deployed proposed segmentation model in a federated environment. [ABSTRACT FROM AUTHOR]

Deep kernel learning has been well explored for multi-class classification tasks; however, relatively less work is done for one-class classification (OCC). OCC needs samples from only one class to train the model. Most recently, kernel regularized least squares (KRL) method-based deep architecture is developed for the OCC task. This paper introduces a novel extension of this method by embedding minimum variance information within this architecture. This embedding improves the generalization capability of the classifier by reducing the intra-class variance. In contrast to traditional deep learning methods, this method can effectively work with small-size datasets. We conduct a comprehensive set of experiments on 18 benchmark datasets (13 biomedical and 5 other datasets) to demonstrate the performance of the proposed classifier. We compare the results with 16 state-of-the-art one-class classifiers. Further, we also test our method for 2 real-world biomedical datasets viz.; detection of Alzheimer's disease from structural magnetic resonance imaging data and detection of breast cancer from histopathological images. Proposed method exhibits more than 5% F 1 score compared to existing state-of-the-art methods for various biomedical benchmark datasets. This makes it viable for application in biomedical fields where relatively less amount of data is available. [ABSTRACT FROM AUTHOR]

Structural magnetic resonance imaging (sMRI) has been used to examine age-related neuroanatomical changes in the human brain. In the present work, a pre-trained deep learning model and an ensemble deep random vector functional link (edRVFL) classifier have been used to create a brain age classification framework from magnetic resonance imaging (MRI) scans. A total of 155 MRI scans of the brain are obtained from the open-access OpenNeuro database and categorized into three age groups (3–5 years old, 7–12 years old, and 18–40 years old). To visualize the age connection across different brain regions, all MRI scans are first segmented into Gray Matter (GM), White Matter (WM), and Cerebrospinal Fluid (CSF). The ResNet-50 network is used to extract features from MRI images, while the edRVFL network is used to classify the retrieved features. Classification accuracy for GM, WM, CSF, and whole brain images are 96.11%, 98.33%, 93.33%, and 94.00%, respectively, using the edRVFL classifier. Region-wise analysis has also been done using Pearson's correlation coefficient (r), coefficient of determination (R 2), and root mean square error (RMSE) to analyze the relationship between brain age and brain tissue volumes. According to the findings of the suggested deep model for brain age categorization, and region-wise analysis, alterations in WM volume are strongly linked to brain aging. [ABSTRACT FROM AUTHOR]