AI Medical Compendium Topic:
Magnetic Resonance Imaging

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Cooperative multi-task learning and interpretable image biomarkers for glioma grading and molecular subtyping.

Medical image analysis
Deep learning methods have been widely used for various glioma predictions. However, they are usually task-specific, segmentation-dependent and lack of interpretable biomarkers. How to accurately predict the glioma histological grade and molecular su...

Style mixup enhanced disentanglement learning for unsupervised domain adaptation in medical image segmentation.

Medical image analysis
Unsupervised domain adaptation (UDA) has shown impressive performance by improving the generalizability of the model to tackle the domain shift problem for cross-modality medical segmentation. However, most of the existing UDA approaches depend on hi...

Diagnosis of Autism Spectrum Disorder (ASD) by Dynamic Functional Connectivity Using GNN-LSTM.

Sensors (Basel, Switzerland)
Early detection of autism spectrum disorder (ASD) is particularly important given its insidious qualities and the high cost of the diagnostic process. Currently, static functional connectivity studies have achieved significant results in the field of...

A density-based MS disease diagnosis model using the capuchin search algorithm and an ensemble of deep neural networks.

Scientific reports
Multiple sclerosis (MS) is a severe brain disease that permanently destroys brain cells, impacting vision, balance, muscle control, and daily activity. This research employs a weighted combination of deep neural networks and optimization techniques f...

Deep learning models reveal the link between dynamic brain connectivity patterns and states of consciousness.

Scientific reports
Decoding states of consciousness from brain activity is a central challenge in neuroscience. Dynamic functional connectivity (dFC) allows the study of short-term temporal changes in functional connectivity (FC) between distributed brain areas. By clu...

Predicting local control of brain metastases after stereotactic radiotherapy with clinical, radiomics and deep learning features.

Radiation oncology (London, England)
BACKGROUND AND PURPOSE: Timely identification of local failure after stereotactic radiotherapy for brain metastases allows for treatment modifications, potentially improving outcomes. While previous studies showed that adding radiomics or Deep Learni...

Development and validation of machine learning algorithms for early detection of ankylosing spondylitis using magnetic resonance images.

Technology and health care : official journal of the European Society for Engineering and Medicine
BackgroundAnkylosing spondylitis (AS) is a chronic inflammatory disease affecting the sacroiliac joints and spine, often leading to disability if not diagnosed and treated early.ObjectiveIn this study, we present the development and validation of mac...

Detecting the articular disk in magnetic resonance images of the temporomandibular joint using YOLO series.

Dental materials journal
The purpose of this study was to construct an artificial intelligence object detection model to detect the articular disk from temporomandibular joint (TMJ) magnetic resonance (MR) images using YOLO series. The study included two experiments using da...