Titlebook: Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology; Third International Seyed Mostafa Kia,Hassan Mohy-ud-Din,Ma

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Multiple Sclerosis Lesion Segmentation Using Longitudinal Normalization and Convolutional Recurrent d inflammatory activities are examined by longitudinal image analysis to support diagnosis and treatment decision. Automated lesion segmentation methods based on deep convolutional neural networks (CNN) have been proposed, but are not yet applied in the clinical setting. Typical CNNs working on cros

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只看該作者 · 發(fā)表于 2025-3-27 13:48:15

A Deep Transfer Learning Framework for 3D Brain Imaging Based on Optimal Mass Transportuirements to optimize performance. In this study, we propose a deep transfer learning network based on Optimal Mass Transport (OMTNet) for 3D brain image classification using MRI scans from the UK Biobank. The major contributions of the OMTNet method include: a way to map?3D surface-based vertex-wis

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只看該作者 · 發(fā)表于 2025-3-28 01:29:01

Bidirectional Modeling and Analysis of Brain Aging with Normalizing Flowsg able to generate age-specific brain morphology templates that realistically represent the typical aging trend in a healthy population. This work is a step towards unified modeling of functional relationships between 3D brain morphology and clinical variables of interest with powerful normalizing f

只看該作者 · 發(fā)表于 2025-3-28 02:53:53

A Multi-task Deep Learning Framework to Localize the Eloquent Cortex in Brain Tumor Patients Using Dional deep learning approaches and can identify bilateral language areas even when trained on left-hemisphere lateralized cases. Hence, our method may ultimately be useful for preoperative mapping in tumor patients.

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只看該作者 · 發(fā)表于 2025-3-28 12:56:44

An Anatomically-Informed 3D CNN for Brain Aneurysm Classification with Weak Labelsbutions. To tackle this frequent scenario of inherently imbalanced, spatially skewed data sets, we propose a novel, anatomically-driven approach by using a multi-scale and multi-input 3D Convolutional Neural Network (CNN). We apply our model to 214 subjects (83 patients, 131 controls) who underwent

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