137 citations · 279 across the 9 of their papers we have counts for
10 papers
Correlation via synthesis: end-to-end nodule image generation and radiogenomic map learning based on generative adversarial network
Ziyue Xu, Xiaosong Wang, Hoo-Chang Shin +5
Radiogenomic map linking image features and gene expression profiles is useful for noninvasively identifying molecular properties of a particular type of disease. Conventionally, s…
When Unseen Domain Generalization is Unnecessary? Rethinking Data Augmentation
Ling Zhang, Xiaosong Wang, Dong Yang +7
Recent advances in deep learning for medical image segmentation demonstrate expert-level accuracy. However, in clinically realistic environments, such methods have marginal perform…
On the influence of Dice loss function in multi-class organ segmentation of abdominal CT using 3D fully convolutional networks
Chen Shen, Holger R. Roth, Hirohisa Oda +4
Deep learning-based methods achieved impressive results for the segmentation of medical images. With the development of 3D fully convolutional networks (FCNs), it has become feasib…
Towards Automatic Abdominal Multi-Organ Segmentation in Dual Energy CT using Cascaded 3D Fully Convolutional Network
Shuqing Chen, Holger Roth, Sabrina Dorn +7
Automatic multi-organ segmentation of the dual energy computed tomography (DECT) data can be beneficial for biomedical research and clinical applications. However, it is a challeng…
Comparison of the Deep-Learning-Based Automated Segmentation Methods for the Head Sectioned Images of the Virtual Korean Human Project
Mohammad Eshghi, Holger R. Roth, Masahiro Oda +2
This paper presents an end-to-end pixelwise fully automated segmentation of the head sectioned images of the Visible Korean Human (VKH) project based on Deep Convolutional Neural N…
Multi-scale Image Fusion Between Pre-operative Clinical CT and X-ray Microtomography of Lung Pathology
Holger R. Roth, Kai Nagara, Hirohisa Oda +4
Computational anatomy allows the quantitative analysis of organs in medical images. However, most analysis is constrained to the millimeter scale because of the limited resolution…