AIMC Topic: Magnetic Resonance Imaging

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CNN-LSTM Based Multimodal MRI and Clinical Data Fusion for Predicting Functional Outcome in Stroke Patients.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Clinical outcome prediction plays an important role in stroke patient management. From a machine learning point-of-view, one of the main challenges is dealing with heterogeneous data at patient admission, i.e. the image data which are multidimensiona...

Neural Transformers for Intraductal Papillary Mucosal Neoplasms (IPMN) Classification in MRI images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Early detection of precancerous cysts or neoplasms, i.e., Intraductal Papillary Mucosal Neoplasms (IPMN), in pancreas is a challenging and complex task, and it may lead to a more favourable outcome. Once detected, grading IPMNs accurately is also nec...

Beyond the Brain: MIDS Extends BIDS to Multiple Modalities and Anatomical Regions.

Studies in health technology and informatics
Brain Imaging Data Structure (BIDS) provides a valuable tool to organise brain imaging data into a clear and easy standard directory structure. Moreover, BIDS is widely supported by the scientific community and has been established as a powerful stan...

Noninvasive Glioma Grading with Deep Learning: A Pilot Study.

Studies in health technology and informatics
Gliomas are the most common neuroepithelial brain tumors, different by various biological tissue types and prognosis. They could be graded with four levels according to the 2007 WHO classification. The emergence of non-invasive histological and molec...

Beyond the Brain: MIDS Extends BIDS to Multiple Modalities and Anatomical Regions.

Studies in health technology and informatics
Brain Imaging Data Structure (BIDS) provides a valuable tool to organise brain imaging data into a clear and easy standard directory structure. Moreover, BIDS is widely supported by the scientific community and has been established as a powerful stan...

Improving the noninvasive classification of glioma genetic subtype with deep learning and diffusion-weighted imaging.

Neuro-oncology
BACKGROUND: Diagnostic classification of diffuse gliomas now requires an assessment of molecular features, often including IDH-mutation and 1p19q-codeletion status. Because genetic testing requires an invasive process, an alternative noninvasive appr...

TF-Unet:An automatic cardiac MRI image segmentation method.

Mathematical biosciences and engineering : MBE
Personalized heart models are widely used to study the mechanisms of cardiac arrhythmias and have been used to guide clinical ablation of different types of arrhythmias in recent years. MRI images are now mostly used for model building. In cardiac mo...

Deep learning quantification of vascular pharmacokinetic parameters in mouse brain tumor models.

Frontiers in bioscience (Landmark edition)
BACKGROUND: Dynamic contrast-enhanced (DCE) MRI is widely used to assess vascular perfusion and permeability in cancer. In small animal applications, conventional modeling of pharmacokinetic (PK) parameters from DCE MRI images is complex and time con...

Deep learning-based identification of genetic variants: application to Alzheimer's disease classification.

Briefings in bioinformatics
Deep learning is a promising tool that uses nonlinear transformations to extract features from high-dimensional data. Deep learning is challenging in genome-wide association studies (GWAS) with high-dimensional genomic data. Here we propose a novel t...