扩张
发表于 2025-3-28 18:24:21
https://doi.org/10.1007/978-1-349-21601-7resolution patches at different cross sections of the spatial-temporal data and reconstructs high-quality CT volumes. We assess the performance of the network concerning image restoration at different tube currents and multiple resolution scales. The results indicate the ability of our network in re
NOCT
发表于 2025-3-28 19:43:52
https://doi.org/10.1007/0-306-48631-8ibility of important structural details after aggressive denoising. This paper introduces a new CT image denoising method based on the generative adversarial network (GAN) with Wasserstein distance and perceptual similarity. The Wasserstein distance is a key concept of the optimal transport theory,
调色板
发表于 2025-3-28 23:36:17
http://reply.papertrans.cn/27/2646/264591/264591_43.png
Gossamer
发表于 2025-3-29 06:32:53
http://reply.papertrans.cn/27/2646/264591/264591_44.png
ITCH
发表于 2025-3-29 08:19:07
http://reply.papertrans.cn/27/2646/264591/264591_45.png
Certainty
发表于 2025-3-29 14:30:36
http://reply.papertrans.cn/27/2646/264591/264591_46.png
Cloudburst
发表于 2025-3-29 16:00:52
Pancreas Segmentation in CT and MRI via Task-Specific Network Design and Recurrent Neural Contextualomputer-aided screening, diagnosis, and quantitative assessment. Yet, pancreas is a challenging abdominal organ to segment due to the high inter-patient anatomical variability in both shape and volume metrics. Recently, convolutional neural networks (CNN) have demonstrated promising performance on a
visual-cortex
发表于 2025-3-29 20:23:15
Deep Learning for Muscle Pathology Image Analysis critical to guide effective patient treatment since each subtype requires distinct therapy. Image analysis of hematoxylin and eosin (H&E)-stained whole-slide specimens of muscle biopsies are considered as a gold standard for effective IM diagnosis. Accurate segmentation of perimysium plays an impor
Working-Memory
发表于 2025-3-30 01:41:09
2D-Based Coarse-to-Fine Approaches for Small Target Segmentation in Abdominal CT Scansgans (e.g., .) or neoplasms (e.g., .) is sometimes below satisfaction, arguably because deep networks are easily disrupted by the complex and variable background regions which occupy a large fraction of the input volume. In this chapter, we propose two coarse-to-fine mechanisms which use prediction
RAFF
发表于 2025-3-30 04:07:00
http://reply.papertrans.cn/27/2646/264591/264591_50.png