AIMC Topic: Image Interpretation, Computer-Assisted
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A Cascaded Mutliresolution Ensemble Deep Learning Framework for Large Scale Alzheimer's Disease Detection Using Brain MRIs.
IEEE/ACM transactions on computational biology and bioinformatics
Aug 8, 2024
Alzheimer's is progressive and irreversible type of dementia, which causes degeneration and death of cells and their connections in the brain. AD worsens over time and greatly impacts patients' life and affects their important mental functions, inclu...
A Multi-Classification Accessment Framework for Reproducible Evaluation of Multimodal Learning in Alzheimer's Disease.
IEEE/ACM transactions on computational biology and bioinformatics
Aug 8, 2024
Multimodal learning is widely used in automated early diagnosis of Alzheimer's disease. However, the current studies are based on an assumption that different modalities can provide more complementary information to help classify the samples from the...
Deep learning approaches for the detection of scar presence from cine cardiac magnetic resonance adding derived parametric images.
Medical & biological engineering & computing
Aug 6, 2024
This work proposes a convolutional neural network (CNN) that utilizes different combinations of parametric images computed from cine cardiac magnetic resonance (CMR) images, to classify each slice for possible myocardial scar tissue presence. The CNN...
Boundary-Aware Gradient Operator Network for Medical Image Segmentation.
IEEE journal of biomedical and health informatics
Aug 6, 2024
Medical image segmentation is a crucial task in computer-aided diagnosis. Although convolutional neural networks (CNNs) have made significant progress in the field of medical image segmentation, the convolution kernels of CNNs are optimized from rand...
Enhancing Major Depressive Disorder Diagnosis With Dynamic-Static Fusion Graph Neural Networks.
IEEE journal of biomedical and health informatics
Aug 6, 2024
Major Depressive Disorder (MDD) is a debilitating, complex mental condition with unclear mechanisms hindering diagnostic progress. Research links MDD to abnormal brain connectivity using functional magnetic resonance imaging (fMRI). Yet, existing fMR...
DCNNLFS: A Dilated Convolutional Neural Network With Late Fusion Strategy for Intelligent Classification of Gastric Histopathology Images.
IEEE journal of biomedical and health informatics
Aug 6, 2024
Gastric cancer has a high incidence rate, significantly threatening patients' health. Gastric histopathology images can reliably diagnose related diseases. Still, the data volume of histopathology images is too large, making misdiagnosis or missed di...
LightNet: A Novel Lightweight Convolutional Network for Brain Tumor Segmentation in Healthcare.
IEEE journal of biomedical and health informatics
Aug 6, 2024
Diagnosis, treatment planning, surveillance, and the monitoring of clinical trials for brain diseases all benefit greatly from neuroimaging-based tumor segmentation. Recently, Convolutional Neural Networks (CNNs) have demonstrated promising results i...
DEAF-Net: Detail-Enhanced Attention Feature Fusion Network for Retinal Vessel Segmentation.
Journal of imaging informatics in medicine
Aug 5, 2024
Retinal vessel segmentation is crucial for the diagnosis of ophthalmic and cardiovascular diseases. However, retinal vessels are densely and irregularly distributed, with many capillaries blending into the background, and exhibit low contrast. Moreov...
Multi-step framework for glaucoma diagnosis in retinal fundus images using deep learning.
Medical & biological engineering & computing
Aug 5, 2024
Glaucoma is one of the most common causes of blindness in the world. Screening glaucoma from retinal fundus images based on deep learning is a common method at present. In the diagnosis of glaucoma based on deep learning, the blood vessels within the...
Brain-GCN-Net: Graph-Convolutional Neural Network for brain tumor identification.
Computers in biology and medicine
Aug 5, 2024
BACKGROUND: The intersection of artificial intelligence and medical image analysis has ushered in a new era of innovation and changed the landscape of brain tumor detection and diagnosis. Correct detection and classification of brain tumors based on ...
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