Search Results for author: Meirui Jiang

Found 12 papers, 9 papers with code

Client-Level Differential Privacy via Adaptive Intermediary in Federated Medical Imaging

1 code implementation24 Jul 2023 Meirui Jiang, Yuan Zhong, Anjie Le, Xiaoxiao Li, Qi Dou

In this paper, we propose to optimize the trade-off under the context of client-level DP, which focuses on privacy during communications.

Federated Learning

FedSoup: Improving Generalization and Personalization in Federated Learning via Selective Model Interpolation

1 code implementation20 Jul 2023 Minghui Chen, Meirui Jiang, Qi Dou, Zehua Wang, Xiaoxiao Li

In this paper, we propose a novel federated model soup method (i. e., selective interpolation of model parameters) to optimize the trade-off between local and global performance.

Federated Learning Image Classification +1

Fair Federated Medical Image Segmentation via Client Contribution Estimation

no code implementations CVPR 2023 Meirui Jiang, Holger R Roth, Wenqi Li, Dong Yang, Can Zhao, Vishwesh Nath, Daguang Xu, Qi Dou, Ziyue Xu

Recent studies have investigated how to reward clients based on their contribution (collaboration fairness), and how to achieve uniformity of performance across clients (performance fairness).

Fairness Federated Learning +3

Federated Domain Generalization for Image Recognition via Cross-Client Style Transfer

1 code implementation3 Oct 2022 Junming Chen, Meirui Jiang, Qi Dou, Qifeng Chen

Our style representation is exceptionally lightweight and can hardly be used for the reconstruction of the dataset.

Domain Generalization Federated Learning +1

Dynamic Bank Learning for Semi-supervised Federated Image Diagnosis with Class Imbalance

1 code implementation27 Jun 2022 Meirui Jiang, Hongzheng Yang, Xiaoxiao Li, Quande Liu, Pheng-Ann Heng, Qi Dou

Despite recent progress on semi-supervised federated learning (FL) for medical image diagnosis, the problem of imbalanced class distributions among unlabeled clients is still unsolved for real-world use.

Federated Learning

DLTTA: Dynamic Learning Rate for Test-time Adaptation on Cross-domain Medical Images

1 code implementation27 May 2022 Hongzheng Yang, Cheng Chen, Meirui Jiang, Quande Liu, Jianfeng Cao, Pheng Ann Heng, Qi Dou

Based on this estimated discrepancy, a dynamic learning rate adjustment strategy is then developed to achieve a suitable degree of adaptation for each test sample.

Histopathological Image Classification Image Classification +2

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

1 code implementation22 Apr 2022 Sarthak Pati, Ujjwal Baid, Brandon Edwards, Micah Sheller, Shih-han Wang, G Anthony Reina, Patrick Foley, Alexey Gruzdev, Deepthi Karkada, Christos Davatzikos, Chiharu Sako, Satyam Ghodasara, Michel Bilello, Suyash Mohan, Philipp Vollmuth, Gianluca Brugnara, Chandrakanth J Preetha, Felix Sahm, Klaus Maier-Hein, Maximilian Zenk, Martin Bendszus, Wolfgang Wick, Evan Calabrese, Jeffrey Rudie, Javier Villanueva-Meyer, Soonmee Cha, Madhura Ingalhalikar, Manali Jadhav, Umang Pandey, Jitender Saini, John Garrett, Matthew Larson, Robert Jeraj, Stuart Currie, Russell Frood, Kavi Fatania, Raymond Y Huang, Ken Chang, Carmen Balana, Jaume Capellades, Josep Puig, Johannes Trenkler, Josef Pichler, Georg Necker, Andreas Haunschmidt, Stephan Meckel, Gaurav Shukla, Spencer Liem, Gregory S Alexander, Joseph Lombardo, Joshua D Palmer, Adam E Flanders, Adam P Dicker, Haris I Sair, Craig K Jones, Archana Venkataraman, Meirui Jiang, Tiffany Y So, Cheng Chen, Pheng Ann Heng, Qi Dou, Michal Kozubek, Filip Lux, Jan Michálek, Petr Matula, Miloš Keřkovský, Tereza Kopřivová, Marek Dostál, Václav Vybíhal, Michael A Vogelbaum, J Ross Mitchell, Joaquim Farinhas, Joseph A Maldjian, Chandan Ganesh Bangalore Yogananda, Marco C Pinho, Divya Reddy, James Holcomb, Benjamin C Wagner, Benjamin M Ellingson, Timothy F Cloughesy, Catalina Raymond, Talia Oughourlian, Akifumi Hagiwara, Chencai Wang, Minh-Son To, Sargam Bhardwaj, Chee Chong, Marc Agzarian, Alexandre Xavier Falcão, Samuel B Martins, Bernardo C A Teixeira, Flávia Sprenger, David Menotti, Diego R Lucio, Pamela Lamontagne, Daniel Marcus, Benedikt Wiestler, Florian Kofler, Ivan Ezhov, Marie Metz, Rajan Jain, Matthew Lee, Yvonne W Lui, Richard McKinley, Johannes Slotboom, Piotr Radojewski, Raphael Meier, Roland Wiest, Derrick Murcia, Eric Fu, Rourke Haas, John Thompson, David Ryan Ormond, Chaitra Badve, Andrew E Sloan, Vachan Vadmal, Kristin Waite, Rivka R Colen, Linmin Pei, Murat AK, Ashok Srinivasan, J Rajiv Bapuraj, Arvind Rao, Nicholas Wang, Ota Yoshiaki, Toshio Moritani, Sevcan Turk, Joonsang Lee, Snehal Prabhudesai, Fanny Morón, Jacob Mandel, Konstantinos Kamnitsas, Ben Glocker, Luke V M Dixon, Matthew Williams, Peter Zampakis, Vasileios Panagiotopoulos, Panagiotis Tsiganos, Sotiris Alexiou, Ilias Haliassos, Evangelia I Zacharaki, Konstantinos Moustakas, Christina Kalogeropoulou, Dimitrios M Kardamakis, Yoon Seong Choi, Seung-Koo Lee, Jong Hee Chang, Sung Soo Ahn, Bing Luo, Laila Poisson, Ning Wen, Pallavi Tiwari, Ruchika Verma, Rohan Bareja, Ipsa Yadav, Jonathan Chen, Neeraj Kumar, Marion Smits, Sebastian R van der Voort, Ahmed Alafandi, Fatih Incekara, Maarten MJ Wijnenga, Georgios Kapsas, Renske Gahrmann, Joost W Schouten, Hendrikus J Dubbink, Arnaud JPE Vincent, Martin J van den Bent, Pim J French, Stefan Klein, Yading Yuan, Sonam Sharma, Tzu-Chi Tseng, Saba Adabi, Simone P Niclou, Olivier Keunen, Ann-Christin Hau, Martin Vallières, David Fortin, Martin Lepage, Bennett Landman, Karthik Ramadass, Kaiwen Xu, Silky Chotai, Lola B Chambless, Akshitkumar Mistry, Reid C Thompson, Yuriy Gusev, Krithika Bhuvaneshwar, Anousheh Sayah, Camelia Bencheqroun, Anas Belouali, Subha Madhavan, Thomas C Booth, Alysha Chelliah, Marc Modat, Haris Shuaib, Carmen Dragos, Aly Abayazeed, Kenneth Kolodziej, Michael Hill, Ahmed Abbassy, Shady Gamal, Mahmoud Mekhaimar, Mohamed Qayati, Mauricio Reyes, Ji Eun Park, Jihye Yun, Ho Sung Kim, Abhishek Mahajan, Mark Muzi, Sean Benson, Regina G H Beets-Tan, Jonas Teuwen, Alejandro Herrera-Trujillo, Maria Trujillo, William Escobar, Ana Abello, Jose Bernal, Jhon Gómez, Joseph Choi, Stephen Baek, Yusung Kim, Heba Ismael, Bryan Allen, John M Buatti, Aikaterini Kotrotsou, Hongwei Li, Tobias Weiss, Michael Weller, Andrea Bink, Bertrand Pouymayou, Hassan F Shaykh, Joel Saltz, Prateek Prasanna, Sampurna Shrestha, Kartik M Mani, David Payne, Tahsin Kurc, Enrique Pelaez, Heydy Franco-Maldonado, Francis Loayza, Sebastian Quevedo, Pamela Guevara, Esteban Torche, Cristobal Mendoza, Franco Vera, Elvis Ríos, Eduardo López, Sergio A Velastin, Godwin Ogbole, Dotun Oyekunle, Olubunmi Odafe-Oyibotha, Babatunde Osobu, Mustapha Shu'aibu, Adeleye Dorcas, Mayowa Soneye, Farouk Dako, Amber L Simpson, Mohammad Hamghalam, Jacob J Peoples, Ricky Hu, Anh Tran, Danielle Cutler, Fabio Y Moraes, Michael A Boss, James Gimpel, Deepak Kattil Veettil, Kendall Schmidt, Brian Bialecki, Sailaja Marella, Cynthia Price, Lisa Cimino, Charles Apgar, Prashant Shah, Bjoern Menze, Jill S Barnholtz-Sloan, Jason Martin, Spyridon Bakas

Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data.

Boundary Detection Federated Learning

IOP-FL: Inside-Outside Personalization for Federated Medical Image Segmentation

1 code implementation16 Apr 2022 Meirui Jiang, Hongzheng Yang, Chen Cheng, Qi Dou

Federated learning (FL) allows multiple medical institutions to collaboratively learn a global model without centralizing client data.

Federated Learning Image Segmentation +2

HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Images

1 code implementation20 Dec 2021 Meirui Jiang, ZiRui Wang, Qi Dou

Multiple medical institutions collaboratively training a model using federated learning (FL) has become a promising solution for maximizing the potential of data-driven models, yet the non-independent and identically distributed (non-iid) data in medical images is still an outstanding challenge in real-world practice.

Federated Learning Image Classification +1

UniFed: A Unified Framework for Federated Learning on Non-IID Image Features

no code implementations19 Oct 2021 Meirui Jiang, Xiaoxiao Li, Xiaofei Zhang, Michael Kamp, Qi Dou

In this work, we propose a unified framework to tackle the non-iid issues for internal and external clients together.

Domain Generalization Federated Learning +1

FedBN: Federated Learning on Non-IID Features via Local Batch Normalization

4 code implementations ICLR 2021 Xiaoxiao Li, Meirui Jiang, Xiaofei Zhang, Michael Kamp, Qi Dou

The emerging paradigm of federated learning (FL) strives to enable collaborative training of deep models on the network edge without centrally aggregating raw data and hence improving data privacy.

Autonomous Driving Federated Learning

Cannot find the paper you are looking for? You can Submit a new open access paper.