Search Results for author: Weihua Zhou

Found 23 papers, 2 papers with code

MLA-BIN: Model-level Attention and Batch-instance Style Normalization for Domain Generalization of Federated Learning on Medical Image Segmentation

no code implementations29 Jun 2023 Fubao Zhu, Yanhui Tian, Chuang Han, Yanting Li, Jiaofen Nan, Ni Yao, Weihua Zhou

However, there is a problem of domain generalization (DG) in the actual de-ployment, that is, the performance of the model trained by FL in unseen domains will decrease.

Domain Generalization Federated Learning +3

A new method using deep transfer learning on ECG to predict the response to cardiac resynchronization therapy

no code implementations2 Jun 2023 Zhuo He, Hongjin Si, Xinwei Zhang, Qing-Hui Chen, Jiangang Zou, Weihua Zhou

The model was fine-tuned to extract relevant features from the ECG images, and then tested on our dataset of CRT patients to predict their response.

Specificity Transfer Learning

Coronary Artery Semantic Labeling using Edge Attention Graph Matching Network

no code implementations21 May 2023 Chen Zhao, Zhihui Xu, Guang-Uei Hung, Weihua Zhou

The presence of atherosclerotic lesions in coronary arteries is the underlying pathophysiological basis of CAD, and accurate extraction of individual arterial branches using invasive coronary angiography (ICA) is crucial for stenosis detection and CAD diagnosis.

Graph Matching Semantic Segmentation

AGMN: Association Graph-based Graph Matching Network for Coronary Artery Semantic Labeling on Invasive Coronary Angiograms

no code implementations11 Jan 2023 Chen Zhao, Zhihui Xu, Jingfeng Jiang, Michele Esposito, Drew Pienta, Guang-Uei Hung, Weihua Zhou

Semantic labeling of coronary arterial segments in invasive coronary angiography (ICA) is important for automated assessment and report generation of coronary artery stenosis in the computer-aided diagnosis of coronary artery disease (CAD).

Graph Matching

A New Hip Fracture Risk Index Derived from FEA-Computed Proximal Femur Fracture Loads and Energies-to-Failure

no code implementations3 Oct 2022 Xuewei Cao, Joyce H Keyak, Sigurdur Sigurdsson, Chen Zhao, Weihua Zhou, Anqi Liu, Thomas Lang, Hong-Wen Deng, Vilmundur Gudnason, Qiuying Sha

The results showed that the average of the area under the receive operating characteristic curve (AUC) using PC1 was always higher than that using all FE parameters combined in the male subjects.

Automatic reorientation by deep learning to generate short axis SPECT myocardial perfusion images

no code implementations7 Aug 2022 Fubao Zhu, Guojie Wang, Chen Zhao, Saurabh Malhotra, Min Zhao, Zhuo He, Jianzhou Shi, Zhixin Jiang, Weihua Zhou

Five-fold cross-validation with 180 stress and 201 rest MPIs was used for training and internal validation; the remaining images were used for testing.

Model Optimization Translation

Automatic extraction of coronary arteries using deep learning in invasive coronary angiograms

no code implementations24 Jun 2022 Yinghui Meng, Zhenglong Du, Chen Zhao, Minghao Dong, Drew Pienta, Zhihui Xu, Weihua Zhou

A deep learning model U-Net 3+, which incorporates the full-scale skip connections and deep supervisions, was proposed for automatic extraction of coronary arteries from ICAs.

Decision Making Transfer Learning

A Novel Architecture Slimming Method for Network Pruning and Knowledge Distillation

no code implementations21 Feb 2022 Dongqi Wang, Shengyu Zhang, Zhipeng Di, Xin Lin, Weihua Zhou, Fei Wu

A common problem in both pruning and distillation is to determine compressed architecture, i. e., the exact number of filters per layer and layer configuration, in order to preserve most of the original model capacity.

Knowledge Distillation Model Compression +1

Spatial-temporal V-Net for automatic segmentation and quantification of right ventricles in gated myocardial perfusion SPECT images

1 code implementation11 Oct 2021 Chen Zhao, Shi Shi, Zhuo He, Cheng Wang, Zhongqiang Zhao, Xinli Li, Yanli Zhou, Weihua Zhou

By integrating the spatial features from each cardiac frame of the gated MPS and the temporal features from the sequential cardiac frames of the gated MPS, we developed a Spatial-Temporal V-Net (ST-VNet) for automatic extraction of RV endocardial and epicardial contours.

Automatic Identification of the End-Diastolic and End-Systolic Cardiac Frames from Invasive Coronary Angiography Videos

no code implementations6 Oct 2021 Yinghui Meng, Minghao Dong, Xumin Dai, Haipeng Tang, Chen Zhao, Jingfeng Jiang, Shun Xu, Ying Zhou, Fubao Zhu1, Zhihui Xu, Weihua Zhou

More specifically, a detection algorithm is first used to detect the key points of coronary arteries, and then an optical flow method is employed to track the trajectories of the selected key points.

Anatomy Optical Flow Estimation

A Deep Learning-based Method to Extract Lumen and Media-Adventitia in Intravascular Ultrasound Images

no code implementations21 Feb 2021 Fubao Zhu, Zhengyuan Gao, Chen Zhao, Hanlei Zhu, Yong Dong, Jingfeng Jiang, Neng Dai, Weihua Zhou

In this paper, we aim to develop a deep learning-based method using an encoder-decoder deep architecture to automatically extract both lumen and MA border.

A Deep Learning-Based Approach to Extracting Periosteal and Endosteal Contours of Proximal Femur in Quantitative CT Images

no code implementations3 Feb 2021 Yu Deng, Ling Wang, Chen Zhao, Shaojie Tang, Xiaoguang Cheng, Hong-Wen Deng, Weihua Zhou

In this study, we proposed an approach based on deep learning for the automatic extraction of the periosteal and endosteal contours of proximal femur in order to differentiate cortical and trabecular bone compartments.

Interactive Segmentation

A new approach to extracting coronary arteries and detecting stenosis in invasive coronary angiograms

no code implementations25 Jan 2021 Chen Zhao, Haipeng Tang, Daniel McGonigle, Zhuo He, Chaoyang Zhang, Yu-Ping Wang, Hong-Wen Deng, Robert Bober, Weihua Zhou

We aim to develop an automatic algorithm by deep learning to extract coronary arteries from ICAs. In this study, a multi-input and multi-scale (MIMS) U-Net with a two-stage recurrent training strategy was proposed for the automatic vessel segmentation.


A Novel Method for ECG Signal Classification via One-Dimensional Convolutional Neural Network

no code implementations20 Jun 2020 Xuan Hua, Jungang Han, Chen Zhao, Haipeng Tang, Zhuo He, Jinshan Tang, Qing-Hui Chen, Shaojie Tang, Weihua Zhou

This paper presents an end-to-end ECG signal classification method based on a novel segmentation strategy via 1D Convolutional Neural Networks (CNN) to aid the classification of ECG signals.

Classification General Classification

A Deep Learning-Based Method for Automatic Segmentation of Proximal Femur from Quantitative Computed Tomography Images

no code implementations9 Jun 2020 Chen Zhao, Joyce H. Keyak, Jinshan Tang, Tadashi S. Kaneko, Sundeep Khosla, Shreyasee Amin, Elizabeth J. Atkinson, Lan-Juan Zhao, Michael J. Serou, Chaoyang Zhang, Hui Shen, Hong-Wen Deng, Weihua Zhou

During the experiments for the entire cohort then for male and female subjects separately, 90% of the subjects were used in 10-fold cross-validation for training and internal validation, and to select the optimal parameters of the proposed models; the rest of the subjects were used to evaluate the performance of models.

Image Segmentation Semantic Segmentation +1

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