Substance-Abuse
发表于 2025-3-23 13:23:24
http://reply.papertrans.cn/63/6240/623992/623992_11.png
展览
发表于 2025-3-23 16:48:56
http://reply.papertrans.cn/63/6240/623992/623992_12.png
Aphorism
发表于 2025-3-23 20:13:54
http://reply.papertrans.cn/63/6240/623992/623992_13.png
抚育
发表于 2025-3-24 00:41:47
Amanda Reichelt-Brushettre named Reconstruction Swin Transformer (RST) for 4D MRI. RST inherits the backbone design of the Video Swin Transformer with a novel reconstruction head introduced to restore pixel-wise intensity. A convolution network called SADXNet is used for rapid initialization of 2D MR frames before RST lear
GOUGE
发表于 2025-3-24 06:18:53
http://reply.papertrans.cn/63/6240/623992/623992_15.png
overweight
发表于 2025-3-24 10:22:31
Amanda Reichelt-Brushett,Pelli L. Howe,Anthony A. Chariton,Michael St. J. Warneion of interest from the magnetic resonance imaging. Both branches are based on convolutional neural networks. After passing the exams by the two embedding branches, the output feature vectors are concatenated, and a multi-layer perceptron is used to classify the glioma biomarkers as a multi-class p
Project
发表于 2025-3-24 12:33:38
Michelle Devlin,Jon Brodiereduce the total number of registrations required for a patient by an average factor of 27.5 while maintaining comparable registration quality. Additionally composing deformations further reduces the number of registrations by a factor of 1.86, resulting in an overall average reduction factor of 51.
Middle-Ear
发表于 2025-3-24 17:31:25
http://reply.papertrans.cn/63/6240/623992/623992_18.png
LAVA
发表于 2025-3-24 22:35:41
Angela Carpenter,Amanda Reichelt-Brushettnce and explainability of CNN-based classification models. Additionally, we introduce an explainability metric to quantitatively evaluate the alignment of model attention with radiologist-specified regions of interest (ROIs). We demonstrate that combining the radiology reports with chest X-ray image
strdulate
发表于 2025-3-25 01:15:36
Michael St. J. Warne,Amanda Reichelt-Brushettnce and explainability of CNN-based classification models. Additionally, we introduce an explainability metric to quantitatively evaluate the alignment of model attention with radiologist-specified regions of interest (ROIs). We demonstrate that combining the radiology reports with chest X-ray image