AIMC Topic: Magnetic Resonance Imaging
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A Multi-Modal Pelvic MRI Dataset for Deep Learning-Based Pelvic Organ Segmentation in Endometriosis.
Scientific data
Jul 24, 2025
Endometriosis affects approximately 190 million females of reproductive age worldwide. Magnetic Resonance Imaging (MRI) has been recommended as the primary non-invasive diagnostic method for endometriosis. This study presents new female pelvic MRI mu...
Post-stroke aphasia analysis using topological alterations in brain functional networks.
Journal of neural engineering
Jul 24, 2025
. Nearly one-third of stroke patients develop aphasia. Although the function of classical language areas (e.g. Broca's area, Wernicke's area) has been widely characterized, the network reorganization mechanisms behind specific language dysfunctions i...
Normal Pressure Hydrocephalus Classification using Weakly-Supervised Local Feature Extraction.
Computers in biology and medicine
Jul 23, 2025
Normal Pressure Hydrocephalus (NPH) presents diagnostic challenges because its symptoms often overlap with other neurological conditions. A key radiological NPH indicator is ventricular cerebrospinal fluid (CSF) volume, assessed by neuroradiologists ...
Preoperative MRI-based radiomics analysis of intra- and peritumoral regions for predicting CD3 expression in early cervical cancer.
Scientific reports
Jul 23, 2025
The study investigates the correlation between CD3 T-cell expression levels and cervical cancer (CC) while developing a magnetic resonance (MR) imaging-based radiomics model for preoperative prediction of CD3 T-cell expression levels. Prognostic corr...
Multi-scale interaction and locally enhanced bridging network for medical image segmentation.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Jul 22, 2025
Accurate organ segmentation is crucial for precise medical diagnosis. Recent methods in CNNs and Transformers have significantly enhanced automatic medical image segmentation. Their encoders and decoders often rely on simple skip connections, which f...
Machine learning approach effectively discriminates between Parkinson's disease and progressive supranuclear palsy: Multi-level indices of rs-fMRI.
Brain research bulletin
Jul 22, 2025
AIM: Parkinson's disease (PD) and progressive supranuclear palsy (PSP) present similar clinical symptoms, but their treatment options and clinical prognosis differ significantly. Therefore, we aimed to discriminate between PD and PSP based on multi-l...
MinT: Magnetic resonance image unsupervised translation via decoupling anatomical structure and contrast.
Computers in biology and medicine
Jul 22, 2025
Unsupervised image-to-image translation, which synthesizes new images from existing ones, has become a prominent research topic in computer vision. This technique is particularly valuable in the magnetic resonance (MR) imaging domain, where acquiring...
ASD-GraphNet: A novel graph learning approach for Autism Spectrum Disorder diagnosis using fMRI data.
Computers in biology and medicine
Jul 21, 2025
Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition with heterogeneous symptomatology, making accurate diagnosis challenging. Traditional methods rely on subjective behavioral assessments, often overlooking subtle neural biomarke...
Deep learning using nasal endoscopy and T2-weighted MRI for prediction of sinonasal inverted papilloma-associated squamous cell carcinoma: an exploratory study.
European radiology experimental
Jul 21, 2025
BACKGROUND: Detecting malignant transformation of sinonasal inverted papilloma (SIP) into squamous cell carcinoma (SIP-SCC) before surgery is a clinical need. We aimed to explore the value of deep learning (DL) that leverages nasal endoscopy and T2-w...
Enhancing cardiac disease detection via a fusion of machine learning and medical imaging.
Scientific reports
Jul 19, 2025
Cardiovascular illnesses continue to be a predominant cause of mortality globally, underscoring the necessity for prompt and precise diagnosis to mitigate consequences and healthcare expenditures. This work presents a complete hybrid methodology that...
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