AIMC Topic: Magnetic Resonance Imaging
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Enhancing efficient deep learning models with multimodal, multi-teacher insights for medical image segmentation.
Scientific reports
May 7, 2025
The rapid evolution of deep learning has dramatically enhanced the field of medical image segmentation, leading to the development of models with unprecedented accuracy in analyzing complex medical images. Deep learning-based segmentation holds signi...
OA-HybridCNN (OHC): An advanced deep learning fusion model for enhanced diagnostic accuracy in knee osteoarthritis imaging.
PloS one
May 7, 2025
Knee osteoarthritis (KOA) is a leading cause of disability globally. Early and accurate diagnosis is paramount in preventing its progression and improving patients' quality of life. However, the inconsistency in radiologists' expertise and the onset ...
Enhancing Neurodegenerative Disease Diagnosis Through Confidence-Driven Dynamic Spatio-Temporal Convolutional Network.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
May 7, 2025
Dynamic brain networks are more effective than static networks in characterizing the evolving patterns of brain functional connectivity, making them a more promising tool for diagnosing neurodegenerative diseases. However, existing classification met...
A novel transfer learning framework for non-uniform conductivity estimation with limited data in personalized brain stimulation.
Physics in medicine and biology
May 6, 2025
. Personalized transcranial magnetic stimulation (TMS) requires individualized head models that incorporate non-uniform conductivity to enable target-specific stimulation. Accurately estimating non-uniform conductivity in individualized head models r...
Progressive Distillation With Optimal Transport for Federated Incomplete Multi-Modal Learning of Brain Tumor Segmentation.
IEEE journal of biomedical and health informatics
May 6, 2025
Multi-modal Magnetic Resonance Imaging (MRI) provide sufficient complementary information for brain tumor segmentation, however, most current approaches rely on complete modalities and may collapse with incomplete modalities. Moreover, most existing ...
Hypercomplex Graph Neural Network: Towards Deep Intersection of Multi-Modal Brain Networks.
IEEE journal of biomedical and health informatics
May 6, 2025
The multi-modal neuroimage study has provided insights into understanding the heteromodal relationships between brain network organization and behavioral phenotypes. Integrating data from various modalities facilitates the characterization of the int...
Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma From Multi-Sequence Magnetic Resonance Imaging Based on Deep Fusion Representation Learning.
IEEE journal of biomedical and health informatics
May 6, 2025
Recent studies have identified microvascular invasion (MVI) as the most vital independent biomarker associated with early tumor recurrence. With advancements in medical technology, several computational methods have been developed to predict preopera...
Towards High-Quality MRI Reconstruction With Anisotropic Diffusion-Assisted Generative Adversarial Networks and Its Multi-Modal Images Extension.
IEEE journal of biomedical and health informatics
May 6, 2025
Recently, fast Magnetic Resonance Imaging reconstruction technology has emerged as a promising way to improve the clinical diagnostic experience by significantly reducing scan times. While existing studies have used Generative Adversarial Networks to...
Adaptive Cross-Feature Fusion Network With Inconsistency Guidance for Multi-Modal Brain Tumor Segmentation.
IEEE journal of biomedical and health informatics
May 6, 2025
In the context of contemporary artificial intelligence, increasing deep learning (DL) based segmentation methods have been recently proposed for brain tumor segmentation (BraTS) via analysis of multi-modal MRI. However, known DL-based works usually d...
PEARL: Cascaded Self-Supervised Cross-Fusion Learning for Parallel MRI Acceleration.
IEEE journal of biomedical and health informatics
May 6, 2025
Supervised deep learning (SDL) methodology holds promise for accelerated magnetic resonance imaging (AMRI) but is hampered by the reliance on extensive training data. Some self-supervised frameworks, such as deep image prior (DIP), have emerged, elim...
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