AIMC Topic: Magnetic Resonance Imaging

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MHAGuideNet: a 3D pre-trained guidance model for Alzheimer's Disease diagnosis using 2D multi-planar sMRI images.

BMC medical imaging
BACKGROUND: Alzheimer's Disease is a neurodegenerative condition leading to irreversible and progressive brain damage, with possible features such as structural atrophy. Effective precision diagnosis is crucial for slowing disease progression and red...

Leveraging transfer learning-driven convolutional neural network-based semantic segmentation model for medical image analysis using MRI images.

Scientific reports
Recognition and segmentation of brain tumours (BT) using MR images are valuable and tedious processes in the healthcare industry. Earlier diagnosis and localization of BT provide timely options to select effective treatment plans for the doctors and ...

Deep Learning techniques to detect and analysis of multiple sclerosis through MRI: A systematic literature review.

Computers in biology and medicine
Deep learning (DL) techniques represent a rapidly advancing field within artificial intelligence, gaining significant prominence in the detection and analysis of various medical conditions through the analysis of medical data. This study presents a s...

Artificial intelligence-enhanced magnetic resonance imaging-based pre-operative staging in patients with endometrial cancer.

International journal of gynecological cancer : official journal of the International Gynecological Cancer Society
OBJECTIVE: Evaluation of prognostic factors is crucial in patients with endometrial cancer for optimal treatment planning and prognosis assessment. This study proposes a deep learning pipeline for tumor and uterus segmentation from magnetic resonance...

Brain networks and intelligence: A graph neural network based approach to resting state fMRI data.

Medical image analysis
Resting-state functional magnetic resonance imaging (rsfMRI) is a powerful tool for investigating the relationship between brain function and cognitive processes as it allows for the functional organization of the brain to be captured without relying...

A Fusion Model of MRI Deep Transfer Learning and Radiomics for Discriminating between Pilocytic Astrocytoma and Adamantinomatous Craniopharyngioma.

Academic radiology
RATIONALE AND OBJECTIVES: This study aimed to develop and validate a fusion model combining MRI deep transfer learning (DTL) and radiomics for discriminating between pilocytic astrocytoma (PA) and adamantinomatous craniopharyngioma (ACP) in the sella...

Noncontrast MRI-based machine learning and radiomics signature can predict the severity of primary lower limb lymphedema.

Journal of vascular surgery. Venous and lymphatic disorders
OBJECTIVE: According to International Lymphology Society guidelines, the severity of lymphedema is determined by the difference in volume between the affected limb and the healthy side divided by the volume of the healthy side. However, this method o...

Interpretable deep learning survival predictions in sporadic Creutzfeldt-Jakob disease.

Journal of neurology
BACKGROUND: Sporadic Creutzfeldt-Jakob disease (sCJD) is a rapidly progressive and fatal prion disease with significant public health implications. Survival is heterogenous, posing challenges for prognostication and care planning. We developed a surv...