Titlebook: Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics; Le Lu,Xiaosong Wang,Lin Yang Book 2019 Sprin

擴張

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

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,

調(diào)色板

Gossamer

ITCH

Certainty

Cloudburst

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

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

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