争论
发表于 2025-3-28 16:27:59
http://reply.papertrans.cn/63/6207/620662/620662_41.png
CLASP
发表于 2025-3-28 19:04:27
SeizureNet: Multi-Spectral Deep Feature Learning for Seizure Type Classificationsification. We also show that the high-level feature embeddings learnt by SeizureNet considerably improve the accuracy of smaller networks through knowledge distillation for applications with low-memory constraints.
结束
发表于 2025-3-29 01:01:25
http://reply.papertrans.cn/63/6207/620662/620662_43.png
AROMA
发表于 2025-3-29 03:29:29
Patch-Based Brain Age Estimation from MR Imagesrain age, leading to more anatomically driven and interpretable results, and thus confirming relevant literature which suggests that the ventricles and the hippocampus are the areas that are most informative. In addition, we leverage this knowledge in order to improve the overall performance on the
placebo-effect
发表于 2025-3-29 09:24:42
http://reply.papertrans.cn/63/6207/620662/620662_45.png
gruelling
发表于 2025-3-29 12:11:09
http://reply.papertrans.cn/63/6207/620662/620662_46.png
诗集
发表于 2025-3-29 15:50:47
Multiple Sclerosis Lesion Segmentation Using Longitudinal Normalization and Convolutional Recurrent rm Memory (C-LSTM) networks to incorporate the temporal dimension. To reduce scanner- and protocol dependent variations between single MRI exams, we propose a histogram normalization technique as pre-processing step. The ISBI 2015 challenge data was used for network training and cross-validation..We
endarterectomy
发表于 2025-3-29 20:31:07
Deep Voxel-Guided Morphometry (VGM): Learning Regional Brain Changes in Serial MRIolute Error and Gradient loss outperformed all other tested loss functions. Deep VGM maps showed high similarity to the original VGM maps (SSIM .). This was additionally confirmed by a neurologist analysing the MS lesions. Deep VGM resulted in a 3% lesion error rate compared to the original VGM appr
谈判
发表于 2025-3-30 02:28:40
A Deep Transfer Learning Framework for 3D Brain Imaging Based on Optimal Mass Transportategy to fuse all shape metrics and generate an ensemble classification. We tested the approach in a classification task conducted on 26k participants from the UK Biobank, using body mass index (BMI) thresholds as classification labels (normal vs. obese BMI). Ensemble classification accuracies of 72
right-atrium
发表于 2025-3-30 04:54:50
http://reply.papertrans.cn/63/6207/620662/620662_50.png