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

idiopathic

Generative Low-Dose CT Image Denoisingibility 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,

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LIMN

hematuria

分期付款

Lecture Notes in Computer Sciencee last one contain healthy and pathological pancreases, respectively, and achieve the current state of the art in terms of Dice-S?rensen Coefficient (DSC) on all of them. Especially, on the NIH pancreas dataset, we outperform the previous best by an average of over ., and the worst case is improved

expeditious

輪流

Yu-Yi Ding,Jing-Hua Han,Qi Cao,Chao Liu?from DI2IN within multiple iterations, according to the spatial relationship of vertebrae. Finally, the locations of vertebra are refined and constrained with a learned sparse representation. We evaluate the proposed method on two categories of public databases, 3D CT volumes, and 2D X-ray scans, u

大火

Wei Li,Xuan Zhang,Yi Shen Zhango 3D anisotropic volumes. Such a transfer inherits the desired strong generalization capability for within-slice information while naturally exploiting between-slice information for more effective modeling. We show the effectiveness of the 3D AH-Net on two example medical image analysis?applications

Keratin

Evaluation of Contractor’s Tender Proposalsn. We then present a two-stream ConvNets which directly model and learn the two fundamental processes of tumor growth, i.e., cell invasion and mass effect, and predict the subsequent involvement regions of a tumor. Experiments on a longitudinal?pancreatic tumor data set show that both approaches sub