Liangqiong Qu

Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging

May 17, 2022
Rui Yan , Liangqiong Qu , Qingyue Wei , Shih-Cheng Huang , Liyue Shen , Daniel Rubin , Lei Xing , Yuyin Zhou

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* Code and trained models are available at: https://github.com/rui-yan/SSL-FL

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Masked Co-attentional Transformer reconstructs 100x ultra-fast/low-dose whole-body PET from longitudinal images and anatomically guided MRI

May 09, 2022
Yan-Ran , Wang , Liangqiong Qu , Natasha Diba Sheybani , Xiaolong Luo , Jiangshan Wang , Kristina Elizabeth Hawk , Ashok Joseph Theruvath , Sergios Gatidis , Xuerong Xiao , Allison Pribnow , Daniel Rubin , Heike E. Daldrup-Link

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* This submission has been removed by arXiv administrators because the submitter did not have the right to assign the license at the time of submission

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Learning MRI Artifact Removal With Unpaired Data

Oct 09, 2021
Siyuan Liu , Kim-Han Thung , Liangqiong Qu , Weili Lin , Dinggang Shen , Pew-Thian Yap

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An Experimental Study of Data Heterogeneity in Federated Learning Methods for Medical Imaging

Jul 18, 2021
Liangqiong Qu , Niranjan Balachandar , Daniel L Rubin

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SplitAVG: A heterogeneity-aware federated deep learning method for medical imaging

Jul 06, 2021
Miao Zhang , Liangqiong Qu , Praveer Singh , Jayashree Kalpathy-Cramer , Daniel L. Rubin

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Handling Data Heterogeneity with Generative Replay in Collaborative Learning for Medical Imaging

Jun 24, 2021
Liangqiong Qu , Niranjan Balachandar , Miao Zhang , Daniel Rubin

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Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning

Jun 10, 2021
Liangqiong Qu , Yuyin Zhou , Paul Pu Liang , Yingda Xia , Feifei Wang , Li Fei-Fei , Ehsan Adeli , Daniel Rubin

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Addressing catastrophic forgetting for medical domain expansion

Mar 24, 2021
Sharut Gupta , Praveer Singh , Ken Chang , Liangqiong Qu , Mehak Aggarwal , Nishanth Arun , Ashwin Vaswani , Shruti Raghavan , Vibha Agarwal , Mishka Gidwani , Katharina Hoebel , Jay Patel , Charles Lu , Christopher P. Bridge , Daniel L. Rubin , Jayashree Kalpathy-Cramer

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* First three authors contributed equally

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The unreasonable effectiveness of Batch-Norm statistics in addressing catastrophic forgetting across medical institutions

Nov 16, 2020
Sharut Gupta , Praveer Singh , Ken Chang , Mehak Aggarwal , Nishanth Arun , Liangqiong Qu , Katharina Hoebel , Jay Patel , Mishka Gidwani , Ashwin Vaswani , Daniel L Rubin , Jayashree Kalpathy-Cramer

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* Accepted as oral presentation in Machine Learning for Health (ML4H) at NeurIPS 2020 - Extended Abstract ; 6 pages and 4 figures

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Federated Learning for Breast Density Classification: A Real-World Implementation

Sep 17, 2020
Holger R. Roth , Ken Chang , Praveer Singh , Nir Neumark , Wenqi Li , Vikash Gupta , Sharut Gupta , Liangqiong Qu , Alvin Ihsani , Bernardo C. Bizzo , Yuhong Wen , Varun Buch , Meesam Shah , Felipe Kitamura , Matheus Mendonça , Vitor Lavor , Ahmed Harouni , Colin Compas , Jesse Tetreault , Prerna Dogra , Yan Cheng , Selnur Erdal , Richard White , Behrooz Hashemian , Thomas Schultz , Miao Zhang , Adam McCarthy , B. Min Yun , Elshaimaa Sharaf , Katharina V. Hoebel , Jay B. Patel , Bryan Chen , Sean Ko , Evan Leibovitz , Etta D. Pisano , Laura Coombs , Daguang Xu , Keith J. Dreyer , Ittai Dayan , Ram C. Naidu , Mona Flores , Daniel Rubin , Jayashree Kalpathy-Cramer

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* Accepted at the 1st MICCAI Workshop on "Distributed And Collaborative Learning"; add citation to Fig. 1 & 2 and update Fig. 5

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