AIMC Topic: Magnetic Resonance Imaging
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Integrating radiological and clinical data for clinically significant prostate cancer detection with machine learning techniques.
Scientific reports
Feb 4, 2025
In prostate cancer (PCa), risk calculators have been proposed, relying on clinical parameters and magnetic resonance imaging (MRI) enable early prediction of clinically significant cancer (CsPCa). The prostate imaging-reporting and data system (PI-RA...
CQformer: Learning Dynamics Across Slices in Medical Image Segmentation.
IEEE transactions on medical imaging
Feb 4, 2025
Prevalent studies on deep learning-based 3D medical image segmentation capture the continuous variation across 2D slices mainly via convolution, Transformer, inter-slice interaction, and time series models. In this work, via modeling this variation b...
Leveraging Input-Level Feature Deformation With Guided-Attention for Sulcal Labeling.
IEEE transactions on medical imaging
Feb 4, 2025
The identification of cortical sulci is key for understanding functional and structural development of the cortex. While large, consistent sulci (or primary/secondary sulci) receive significant attention in most studies, the exploration of smaller an...
UTSRMorph: A Unified Transformer and Superresolution Network for Unsupervised Medical Image Registration.
IEEE transactions on medical imaging
Feb 4, 2025
Complicated image registration is a key issue in medical image analysis, and deep learning-based methods have achieved better results than traditional methods. The methods include ConvNet-based and Transformer-based methods. Although ConvNets can eff...
An Explainable Unified Framework of Spatio-Temporal Coupling Learning With Application to Dynamic Brain Functional Connectivity Analysis.
IEEE transactions on medical imaging
Feb 4, 2025
Time-series data such as fMRI and MEG carry a wealth of inherent spatio-temporal coupling relationship, and their modeling via deep learning is essential for uncovering biological mechanisms. However, current machine learning models for mining spatio...
M₂DC: A Meta-Learning Framework for Generalizable Diagnostic Classification of Major Depressive Disorder.
IEEE transactions on medical imaging
Feb 4, 2025
Psychiatric diseases are bringing heavy burdens for both individual health and social stability. The accurate and timely diagnosis of the diseases is essential for effective treatment and intervention. Thanks to the rapid development of brain imaging...
Radiomics Analysis of Different Machine Learning Models based on Multiparametric MRI to Identify Benign and Malignant Testicular Lesions.
Academic radiology
Feb 3, 2025
RATIONALE AND OBJECTIVES: To develop and validate a machine learning-based prediction model for the use of multiparametric magnetic resonance imaging(MRI) to predict benign and malignant lesions in the testis.
Harmonizing flows: Leveraging normalizing flows for unsupervised and source-free MRI harmonization.
Medical image analysis
Feb 2, 2025
Lack of standardization and various intrinsic parameters for magnetic resonance (MR) image acquisition results in heterogeneous images across different sites and devices, which adversely affects the generalization of deep neural networks. To alleviat...
Multi-knowledge informed deep learning model for multi-point prediction of Alzheimer's disease progression.
Neural networks : the official journal of the International Neural Network Society
Feb 1, 2025
The diagnosis of Alzheimer's disease (AD) based on visual features-informed by clinical knowledge has achieved excellent results. Our study endeavors to present an innovative and detailed deep learning framework designed to accurately predict the pro...
SDR-Former: A Siamese Dual-Resolution Transformer for liver lesion classification using 3D multi-phase imaging.
Neural networks : the official journal of the International Neural Network Society
Feb 1, 2025
Automated classification of liver lesions in multi-phase CT and MR scans is of clinical significance but challenging. This study proposes a novel Siamese Dual-Resolution Transformer (SDR-Former) framework, specifically designed for liver lesion class...
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