A proximal augmented Lagrangian based algorithm for federated learning with global and local convex conic constraints

Chuan He, Le Peng, Ju Sun.
Accepted to “ NeurIPS'23 Workshop on Optimization for Machine Learning

Direct Metric optimization for Imbalanced Classification

Le Peng*, Yash Travadi*, Ying Cui, Ju Sun.
Accepted to “ IEEE ICHI Workshop on Ethics and Bias of Artificial Intelligence in Clinical Applications; (EBAIC) 2023

Imbalanced Classification in Medical Imaging via Regrouping

Le Peng, Yash Travadi, Rui Zhang, Ying Cui, Ju Sun.
Accepted to “NeurIPS’22 Workshop: When Medical Imaging Meets NeurIPS” (MedNeurIPS) 2022

Rethinking Transfer Learning in Medical Imaging

Le Peng, Hengyue Liang, Gaoxiang Luo, Taihui Li, Ju Sun.
Accepted to “ British Machine Vision Conference ” (BMVC) 2023 (oral)

Early Stopping beyond Supervised Learning: Self-Validation

Taihui Li, Zhong Zhuang, Hengyue Liang, Le Peng, Hengkang Wang, Ju Sun.
Accepted to “British Machine Vision Conference ” (BMVC) 2021

An In-Depth Evaluation of Federated Learning on Biomedical Natural Language Processing for Information Extraction

Le Peng, Gaoxiang Luo, Sicheng Zhou, Jiandong Chen, Rui Zhang, Ziyue Xu, Ju Sun.
Accepted to “ npj Digital Medicine ” NPJ Digit. Med. 2024

A Systematic Evaluation of Federated Learning on Biomedical Natural Language Processing

Le Peng, Sicheng Zhou, Jiandong Chen, Rui Zhang, Ziyue Xu, Ju Sun.
Accepted to “ KDD 2023 International Workshop on Federated Learning for Distributed Data Mining ” (FL4Data-Mining) 2023

Evaluation of federated learning variations for COVID-19 diagnosis using chest radiographs from 42 US and European hospitals

Le Peng, Gaoxiang Luo, Andrew Walker, Zachary Zaiman, Emma K Jones, Hemant Gupta, Kristopher Kersten, John L Burns, Christopher A Harle, Tanja Magoc, Benjamin Shickel, Scott D Steenburg, Tyler Loftus, Genevieve B Melton, Judy Wawira Gichoya, Ju Sun, Christopher J Tignanelli.
Accepted to “Journal of the American Medical Informatics Association ” (JAMIA) 2022

A Prospective Observational Study to Investigate Performance of a Chest X-ray Artificial Intelligence Diagnostic Support Tool Across 12 U.S. Hospitals

Ju Sun, Le Peng, Taihui Li, Dyah Adila, Zach Zaiman, Genevieve Melton, Nicholas E Ingraham, Eric Murray, Daniel Boley, Sean Switzer, John L Burns, Kun Huang, Tadashi Allen, Scott D Steenburg, Judy Wawira Gichoya, Erich Kummerfeld, Christopher J Tignanelli.
Accepted to “Radiology: Artificial Intelligence ” (Radiology AI) 2021