AIMC Topic: Magnetic Resonance Imaging

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Are Diffusion Models Effective Good Feature Extractors for MRI Discriminative Tasks?

Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
PURPOSE: Diffusion models (DMs) excel in pixel-level and spatial tasks and are proven feature extractors for 2D image discriminative tasks when pretrained. However, their capabilities in 3D MRI discriminative tasks remain largely untapped. This study...

Relevance of choroid plexus volumes in multiple sclerosis.

Fluids and barriers of the CNS
BACKGROUND: The choroid plexus (ChP) plays a pivotal role in inflammatory processes that occur in multiple sclerosis (MS). The enlargement of the ChP in relapsing-remitting multiple sclerosis (RRMS) is considered to be an indication of disease activi...

Radiomic analysis based on machine learning of multi-sequences MR to assess early treatment response in locally advanced nasopharyngeal carcinoma.

Science progress
ObjectiveThe prediction of early response in locally advanced nasopharyngeal carcinoma (LA-NPC) after concurrent chemoradiotherapy (CCRT) is important for determining the need for timely consolidation therapy. We developed a radiomic analysis of mult...

Radiological evaluation and clinical implications of deep learning- and MRI-based synthetic CT for the assessment of cervical spine injuries.

European radiology
OBJECTIVE: Efficient evaluation of soft tissues and bony structures following cervical spine trauma is critical. We sought to evaluate the diagnostic validity of magnetic resonance imaging (MRI)-based synthetic CT (sCT) compared with conventional com...

Deep learning approaches for classification tasks in medical X-ray, MRI, and ultrasound images: a scoping review.

BMC medical imaging
Medical images occupy the largest part of the existing medical information and dealing with them is challenging not only in terms of management but also in terms of interpretation and analysis. Hence, analyzing, understanding, and classifying them, b...

Enhancing efficient deep learning models with multimodal, multi-teacher insights for medical image segmentation.

Scientific reports
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
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
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
. 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
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 ...