Search Results for author: Qi Chang

Found 14 papers, 6 papers with code

Dealing With Heterogeneous 3D MR Knee Images: A Federated Few-Shot Learning Method With Dual Knowledge Distillation

1 code implementation25 Mar 2023 Xiaoxiao He, Chaowei Tan, Bo Liu, Liping Si, Weiwu Yao, Liang Zhao, Di Liu, Qilong Zhangli, Qi Chang, Kang Li, Dimitris N. Metaxas

The supervised learning of the proposed method extracts features from limited labeled data in each client, while the unsupervised data is used to distill both feature and response-based knowledge from a national data repository to further improve the accuracy of the collaborative model and reduce the communication cost.

Federated Learning Few-Shot Learning +1

Autofluorescence Bronchoscopy Video Analysis for Lesion Frame Detection

no code implementations21 Mar 2023 Qi Chang, Rebecca Bascom, Jennifer Toth, Danish Ahmad, William E. Higgins

Because of the significance of bronchial lesions as indicators of early lung cancer and squamous cell carcinoma, a critical need exists for early detection of bronchial lesions.

Lesion Detection

ESFPNet: efficient deep learning architecture for real-time lesion segmentation in autofluorescence bronchoscopic video

1 code implementation15 Jul 2022 Qi Chang, Danish Ahmad, Jennifer Toth, Rebecca Bascom, William E. Higgins

We propose a real-time (processing throughput of 27 frames/sec) deep-learning architecture dubbed ESFPNet for accurate segmentation and robust detection of bronchial lesions in AFB video streams.

Lesion Detection Lesion Segmentation +1

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

no code implementations14 Jun 2022 Qi Chang, Zhennan Yan, Mu Zhou, Di Liu, Khalid Sawalha, Meng Ye, Qilong Zhangli, Mikael Kanski, Subhi Al Aref, Leon Axel, Dimitris Metaxas

Joint 2D cardiac segmentation and 3D volume reconstruction are fundamental to building statistical cardiac anatomy models and understanding functional mechanisms from motion patterns.

3D Reconstruction 3D Shape Reconstruction +5

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

no code implementations22 Jan 2022 Qi Chang, Hui Qu, Zhennan Yan, Yunhe Gao, Lohendran Baskaran, Dimitris Metaxas

Multi-modality images have been widely used and provide comprehensive information for medical image analysis.

Training Federated GANs with Theoretical Guarantees: A Universal Aggregation Approach

1 code implementation9 Feb 2021 Yikai Zhang, Hui Qu, Qi Chang, Huidong Liu, Dimitris Metaxas, Chao Chen

A federatedGAN jointly trains a centralized generator and multiple private discriminators hosted at different sites.

Federated Learning

Multi-modal AsynDGAN: Learn From Distributed Medical Image Data without Sharing Private Information

no code implementations15 Dec 2020 Qi Chang, Zhennan Yan, Lohendran Baskaran, Hui Qu, Yikai Zhang, Tong Zhang, Shaoting Zhang, Dimitris N. Metaxas

As deep learning technologies advance, increasingly more data is necessary to generate general and robust models for various tasks.

Learn distributed GAN with Temporary Discriminators

1 code implementation ECCV 2020 Hui Qu, Yikai Zhang, Qi Chang, Zhennan Yan, Chao Chen, Dimitris Metaxas

Our proposed method tackles the challenge of training GAN in the federated learning manner: How to update the generator with a flow of temporary discriminators?

Federated Learning

Synthetic Learning: Learn From Distributed Asynchronized Discriminator GAN Without Sharing Medical Image Data

1 code implementation CVPR 2020 Qi Chang, Hui Qu, Yikai Zhang, Mert Sabuncu, Chao Chen, Tong Zhang, Dimitris Metaxas

In this paper, we propose a data privacy-preserving and communication efficient distributed GAN learning framework named Distributed Asynchronized Discriminator GAN (AsynDGAN).

Privacy Preserving

Non-Local Graph-Based Prediction For Reversible Data Hiding In Images

no code implementations20 Feb 2018 Qi Chang, Gene Cheung, Yao Zhao, Xiaolong Li, Rongrong Ni

If sufficiently smooth, we pose a maximum a posteriori (MAP) problem using either a quadratic Laplacian regularizer or a graph total variation (GTV) term as signal prior.

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