MINT
发表于 2025-3-26 22:04:56
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不连贯
发表于 2025-3-27 04:08:06
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Obliterate
发表于 2025-3-27 06:48:53
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
Loathe
发表于 2025-3-27 09:26:42
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针叶
发表于 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
奇思怪想
发表于 2025-3-27 21:35:35
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Acetabulum
发表于 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
机警
发表于 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.
inculpate
发表于 2025-3-28 09:16:42
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剧本
发表于 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