AIMC Topic: Magnetic Resonance Imaging
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Innovative machine learning approach for liver fibrosis and disease severity evaluation in MAFLD patients using MRI fat content analysis.
Clinical and experimental medicine
Aug 5, 2025
This study employed machine learning models to quantitatively analyze liver fat content from MRI images for the evaluation of liver fibrosis and disease severity in patients with metabolic dysfunction-associated fatty liver disease (MAFLD). A total o...
Machine learning of whole-brain resting-state fMRI signatures for individualized grading of frontal gliomas.
Cancer imaging : the official publication of the International Cancer Imaging Society
Aug 4, 2025
PURPOSE: Accurate preoperative grading of gliomas is critical for therapeutic planning and prognostic evaluation. We developed a noninvasive machine learning model leveraging whole-brain resting-state functional magnetic resonance imaging (rs-fMRI) b...
Open-radiomics: a collection of standardized datasets and a technical protocol for reproducible radiomics machine learning pipelines.
BMC medical imaging
Aug 4, 2025
BACKGROUND: As an important branch of machine learning pipelines in medical imaging, radiomics faces two major challenges namely reproducibility and accessibility. In this work, we introduce open-radiomics, a set of radiomics datasets along with a co...
Early prediction of proton therapy dose distributions and DVHs for hepatocellular carcinoma using contour-based CNN models from diagnostic CT and MRI.
Radiation oncology (London, England)
Aug 4, 2025
BACKGROUND: Proton therapy is commonly used for treating hepatocellular carcinoma (HCC); however, its feasibility can be challenging to assess in large tumors or those adjacent to critical organs at risk (OARs), which are typically assessed only afte...
TA-SSM net: tri-directional attention and structured state-space model for enhanced MRI-Based diagnosis of Alzheimer's disease and mild cognitive impairment.
BMC medical imaging
Jul 31, 2025
Early diagnosis of Alzheimer's disease (AD) and its precursor, mild cognitive impairment (MCI), is critical for effective prevention and treatment. Computer-aided diagnosis using magnetic resonance imaging (MRI) provides a cost-effective and objectiv...
Identification and validation of an explainable machine learning model for vascular depression diagnosis in the older adults: a multicenter cohort study.
BMC medicine
Jul 31, 2025
BACKGROUND: Vascular depression (VaDep) is a prevalent affective disorder in older adults that significantly impacts functional status and quality of life. Early identification and intervention are crucial but largely insufficient in clinical practic...
A brain tumor segmentation enhancement in MRI images using U-Net and transfer learning.
BMC medical imaging
Jul 31, 2025
This paper presents a novel transfer learning approach for segmenting brain tumors in Magnetic Resonance Imaging (MRI) images. Using Fluid-Attenuated Inversion Recovery (FLAIR) abnormality segmentation masks and MRI scans from The Cancer Genome Atlas...
A successive framework for brain tumor interpretation using Yolo variants.
Scientific reports
Jul 31, 2025
Accurate identification and segmentation of brain tumors in Magnetic Resonance Imaging (MRI) images are critical for timely diagnosis and treatment. MRI is frequently used to diagnose these disorders; however medical professionals find it challenging...
Impact of large language models and vision deep learning models in predicting neoadjuvant rectal score for rectal cancer treated with neoadjuvant chemoradiation.
BMC medical imaging
Jul 31, 2025
This study aims to explore Deep Learning methods, namely Large Language Models (LLMs) and Computer Vision models to accurately predict neoadjuvant rectal (NAR) score for locally advanced rectal cancer (LARC) treated with neoadjuvant chemoradiation (N...
Trauma-predictive brain network connectivity adaptively responds to mild acute stress.
Proceedings of the National Academy of Sciences of the United States of America
Jul 30, 2025
Past traumatic experiences shape neural responses to future stress, but the mechanisms underlying this dynamic interaction remain unclear. Here, we assessed how trauma-related brain networks respond to current acute stress in real time. Using a machi...
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