Titlebook: Machine Learning in Medical Imaging; 9th International Wo Yinghuan Shi,Heung-Il Suk,Mingxia Liu Conference proceedings 2018 Springer Nature

BIBLE

CT Image Enhancement Using Stacked Generative Adversarial Networks and Transfer Learning for Lesionve been made, there is room for continued improvements. One hurdle is that CT images can exhibit high noise and low contrast, particularly in lower dosages. To address this, we focus on a preprocessing method for CT images that uses stacked generative adversarial networks (SGAN) approach. The first

Minatory

mortgage

Regional Abnormality Representation Learning in Structural MRI for AD/MCI Diagnosis,oaches in a unified framework. Specifically, we parcellate a brain into predefined regions by using anatomical knowledge, ., template, and find complex nonlinear relations among voxels, whose intensity denotes the volumetric measure in our case, within each region. Unlike the existing methods that m

刺穿

Joint Registration And Segmentation Of Xray Images Using Generative Adversarial Networks,rning (DL) based approaches tackle the two problems separately without leveraging their mutually beneficial information. We propose a DL based approach for joint registration and segmentation (JRS) of chest Xray images. Generative adversarial networks (GANs) are trained to register a floating image

显而易见

SCCA-Ref: Novel Sparse Canonical Correlation Analysis with Reference to Discover Independent Spatiahether their co-occurrence is due to shared risk factors. Previous work has analyzed univariate associations between individual brain regions but not joint patterns over multiple regions. We propose a new method that jointly analyzes all the regions to discover spatial association patterns between W

ILEUM

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Immortal

Rotation Invariance and Directional Sensitivity: Spherical Harmonics versus Radiomics Features,annot combine the two properties, which are antagonist in simple designs. We propose texture operators based on spherical harmonic wavelets (SHW) invariants and show that they are both LRI and DS. An experimental comparison of SHW and popular radiomics operators for classifying 3D textures reveals t

多骨

Can Dilated Convolutions Capture Ultrasound Video Dynamics?,hallenging task for detecting the standard planes, due to the low-quality data, variability in contrast, appearance and placement of the structures. Conventionally, sequential data is usually modelled with heavy Recurrent Neural Networks (RNNs). In this paper, we propose to apply a convolutional arc

Expertise

Topological Correction of Infant Cortical Surfaces Using Anatomically Constrained U-Net,ly brain development studies. However, infant brain MR images usually exhibit extremely low tissue contrast (especially from 3 to 9 months of age) and dynamic imaging appearance patterns. Thus, it is inevitable to have large amounts of topological errors in the infant brain tissue segmentation resul