51 citations · 249 across the 14 of their papers we have counts for
15 papers
SAINT: Spatially Aware Interpolation NeTwork for Medical Slice Synthesis
Cheng Peng, Wei-An Lin, Haofu Liao +2
Deep learning-based single image super-resolution (SISR) methods face various challenges when applied to 3D medical volumetric data (i.e., CT and MR images) due to the high memory…
Encoding Metal Mask Projection for Metal Artifact Reduction in Computed Tomography
Yuanyuan Lyu, Wei-An Lin, Haofu Liao +2
Metal artifact reduction (MAR) in computed tomography (CT) is a notoriously challenging task because the artifacts are structured and non-local in the image domain. However, they a…
Towards Learning a Self-inverse Network for Bidirectional Image-to-image Translation
Zengming Shen, Yifan Chen, S. Kevin Zhou +3
The one-to-one mapping is necessary for many bidirectional image-to-image translation applications, such as MRI image synthesis as MRI images are unique to the patient. State-of-th…
3D U-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation
Chao Huang, Hu Han, Qingsong Yao +2
Fully convolutional neural networks like U-Net have been the state-of-the-art methods in medical image segmentation. Practically, a network is highly specialized and trained separa…
DuDoNet: Dual Domain Network for CT Metal Artifact Reduction
Wei-An Lin, Haofu Liao, Cheng Peng +5
Computed tomography (CT) is an imaging modality widely used for medical diagnosis and treatment. CT images are often corrupted by undesirable artifacts when metallic implants are c…
Multiview 2D/3D Rigid Registration via a Point-Of-Interest Network for Tracking and Triangulation ()
Haofu Liao, Wei-An Lin, Jiarui Zhang +3
We propose to tackle the problem of multiview 2D/3D rigid registration for intervention via a Point-Of-Interest Network for Tracking and Triangulation (). $\text{PO…
Adversarial Sparse-View CBCT Artifact Reduction
Haofu Liao, Zhimin Huo, William J. Sehnert +2
We present an effective post-processing method to reduce the artifacts from sparsely reconstructed cone-beam CT (CBCT) images. The proposed method is based on the state-of-the-art,…
Less is More: Simultaneous View Classification and Landmark Detection for Abdominal Ultrasound Images
Zhoubing Xu, Yuankai Huo, JinHyeong Park +4
An abdominal ultrasound examination, which is the most common ultrasound examination, requires substantial manual efforts to acquire standard abdominal organ views, annotate the vi…
Select, Attend, and Transfer: Light, Learnable Skip Connections
Saeid Asgari Taghanaki, Aicha Bentaieb, Anmol Sharma +8
Skip connections in deep networks have improved both segmentation and classification performance by facilitating the training of deeper network architectures, and reducing the risk…
Learning to recognize Abnormalities in Chest X-Rays with Location-Aware Dense Networks
Sebastian Guendel, Sasa Grbic, Bogdan Georgescu +4
Chest X-ray is the most common medical imaging exam used to assess multiple pathologies. Automated algorithms and tools have the potential to support the reading workflow, improve…
Learning to Prune Filters in Convolutional Neural Networks
Qiangui Huang, Kevin Zhou, Suya You +1
Many state-of-the-art computer vision algorithms use large scale convolutional neural networks (CNNs) as basic building blocks. These CNNs are known for their huge number of parame…
3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes
Siqi Liu, Daguang Xu, S. Kevin Zhou +7
While deep convolutional neural networks (CNN) have been successfully applied for 2D image analysis, it is still challenging to apply them to 3D anisotropic volumes, especially whe…
A Deep Cascade Network for Unaligned Face Attribute Classification
Hui Ding, Hao Zhou, Shaohua Kevin Zhou +1
Humans focus attention on different face regions when recognizing face attributes. Most existing face attribute classification methods use the whole image as input. Moreover, some…
Automatic Liver Segmentation Using an Adversarial Image-to-Image Network
Dong Yang, Daguang Xu, S. Kevin Zhou +5
Automatic liver segmentation in 3D medical images is essential in many clinical applications, such as pathological diagnosis of hepatic diseases, surgical planning, and postoperati…
Automatic Vertebra Labeling in Large-Scale 3D CT using Deep Image-to-Image Network with Message Passing and Sparsity Regularization
Dong Yang, Tao Xiong, Daguang Xu +10
Automatic localization and labeling of vertebra in 3D medical images plays an important role in many clinical tasks, including pathological diagnosis, surgical planning and postope…