185 citations · 281 across the 10 of their papers we have counts for
10 papers
DeepRecon: Joint 2D Cardiac Segmentation and 3D Volume Reconstruction via A Structure-Specific Generative Method
Qi Chang, Zhennan Yan, Mu Zhou +8
Joint 2D cardiac segmentation and 3D volume reconstruction are fundamental to building statistical cardiac anatomy models and understanding functional mechanisms from motion patter…
TransFusion: Multi-view Divergent Fusion for Medical Image Segmentation with Transformers
Di Liu, Yunhe Gao, Qilong Zhangli +8
Combining information from multi-view images is crucial to improve the performance and robustness of automated methods for disease diagnosis. However, due to the non-alignment char…
A Data-scalable Transformer for Medical Image Segmentation: Architecture, Model Efficiency, and Benchmark
Yunhe Gao, Mu Zhou, Di Liu +3
Transformers have demonstrated remarkable performance in natural language processing and computer vision. However, existing vision Transformers struggle to learn from limited medic…
Modality Bank: Learn multi-modality images across data centers without sharing medical data
Qi Chang, Hui Qu, Zhennan Yan +3
Multi-modality images have been widely used and provide comprehensive information for medical image analysis. However, acquiring all modalities among all institutes is costly and o…
DeepTag: An Unsupervised Deep Learning Method for Motion Tracking on Cardiac Tagging Magnetic Resonance Images
Meng Ye, Mikael Kanski, Dong Yang +5
Cardiac tagging magnetic resonance imaging (t-MRI) is the gold standard for regional myocardium deformation and cardiac strain estimation. However, this technique has not been wide…
Multi-modal AsynDGAN: Learn From Distributed Medical Image Data without Sharing Private Information
Qi Chang, Zhennan Yan, Lohendran Baskaran +5
As deep learning technologies advance, increasingly more data is necessary to generate general and robust models for various tasks. In the medical domain, however, large-scale and…
Learn distributed GAN with Temporary Discriminators
Hui Qu, Yikai Zhang, Qi Chang +3
In this work, we propose a method for training distributed GAN with sequential temporary discriminators. Our proposed method tackles the challenge of training GAN in the federated…
Weakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Images
Hui Qu, Pengxiang Wu, Qiaoying Huang +7
Nuclei segmentation is a fundamental task in histopathology image analysis. Typically, such segmentation tasks require significant effort to manually generate accurate pixel-wise a…
Collaborative Multi-agent Learning for MR Knee Articular Cartilage Segmentation
Chaowei Tan, Zhennan Yan, Shaoting Zhang +2
The 3D morphology and quantitative assessment of knee articular cartilages (i.e., femoral, tibial, and patellar cartilage) in magnetic resonance (MR) imaging is of great importance…
How intelligent are convolutional neural networks?
Zhennan Yan, Xiang Sean Zhou
Motivated by the Gestalt pattern theory, and the Winograd Challenge for language understanding, we design synthetic experiments to investigate a deep learning algorithm's ability t…