Zhennan Yan

Slice2Mesh: 3D Surface Reconstruction From Sparse Slices of Images for the Left Ventricle

Jia Xiao, Wen Zheng, Wenji Wang, Qing Xia et autres

Cine MRI is a widely used technique to evaluate left ventricular function and motion, as it captures temporal information. However, due to the limited spatial resolution, cine MRI only provides a few sparse scans at regular positions and orientations, which poses challenges …

cn, us (code pays fourni par la source)

Automatic detection of pulmonary embolism on computed tomography pulmonary angiogram scan using a three-dimensional convolutional neural network

Huiyuan Zhu, Guangyu Tao, Yifeng Jiang, Linlin Sun et autres

cn, us (code pays fourni par la source)

Publisher Correction: Mining multi-center heterogeneous medical data with distributed synthetic learning

Qi Chang, Zhennan Yan, Mu Zhou, Hui Qu et autres

In this article, Mu Zhou should have been denoted as an equally contributing author. The original article has been corrected.

us, cn, sg, hk, gb, nl (code pays fourni par la source)

AVDNet: Joint coronary artery and vein segmentation with topological consistency

Wenji Wang, Qing Xia, Zhennan Yan, Zhiqiang Hu et autres

Coronary CT angiography (CCTA) is an effective and non-invasive method for coronary artery disease diagnosis. Extracting an accurate coronary artery tree from CCTA image is essential for centerline extraction, plaque detection, and stenosis quantification. In practice, data quality varies. Sometimes, the arteries …

us, cn (code pays fourni par la source)

Mining multi-center heterogeneous medical data with distributed synthetic learning

Qi Chang, Zhennan Yan, Mu Zhou, Hui Qu et autres

Overcoming barriers on the use of multi-center data for medical analytics is challenging due to privacy protection and data heterogeneity in the healthcare system. In this study, we propose the Distributed Synthetic Learning (DSL) architecture to learn across multiple medical centers and …

us, cn, sg, hk, gb, nl (code pays fourni par la source)

Root canal treatment planning by automatic tooth and root canal segmentation in dental CBCT with deep multi-task feature learning

Yiwei Wang, Wenjun Xia, Zhennan Yan, Liang Zhao et autres

cn, us (code pays fourni par la source)

Mining Multi-Center Heterogeneous Medical Data with Distributed Synthetic Learning

Qi Chang, Zhennan Yan, Mu Zhou, Hui Qu et autres

us, sg, cn (code pays fourni par la source)

Modality Bank: Learn multi-modality images across data centers without sharing medical data

Qi Chang, Hui Qu, Zhennan Yan, Yunhe Gao et autres

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 often impossible in clinical settings. To leverage more comprehensive multi-modality information, we propose privacy secured decentralized multi-modality adaptive …

us, sg (code pays fourni par la source)

DeepRecon: Joint 2D Cardiac Segmentation and 3D Volume Reconstruction via A Structure-Specific Generative Method

Qi Chang, Zhennan Yan, Mu Zhou, Di Liu et autres

Joint 2D cardiac segmentation and 3D volume reconstruction are fundamental to building statistical cardiac anatomy models and understanding functional mechanisms from motion patterns. However, due to the low through-plane resolution of cine MR and high inter-subject variance, accurately segmenting cardiac images and …

TransFusion: Multi-view Divergent Fusion for Medical Image Segmentation with Transformers

Di Liu, Yunhe Gao, Qilong Zhangli, Ligong Han et autres

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 characteristics of multi-view images, building correlation and data fusion across views largely remain an open problem. In this …

A Data-scalable Transformer for Medical Image Segmentation: Architecture, Model Efficiency, and Benchmark

Yunhe Gao, Mu Zhou, Di Liu, Zhennan Yan et autres

Transformers have demonstrated remarkable performance in natural language processing and computer vision. However, existing vision Transformers struggle to learn from limited medical data and are unable to generalize on diverse medical image tasks. To tackle these challenges, we present MedFormer, a data-scalable …

Refined Deep Layer Aggregation for Multi-Disease, Multi-View & Multi-Center Cardiac MR Segmentation

Di Liu, Zhennan Yan, Qi Chang, Leon Axel et autres

us (code pays fourni par la source)