Fair Evaluation of Federated Learning Algorithms for Automated Breast Density Classification: The Results of the 2022 ACR-NCI-NVIDIA Federated Learning Challenge
<|reference_start|>Fair Evaluation of Federated Learning Algorithms for Automated Breast Density Classification: The Results of the 2022 ACR-NCI-NVIDIA Federated Learning Challenge: The correct interpretation of breast density is important in the assessment of breast cancer risk. AI has been shown capable of accurately predicting breast density, however, due to the differences in imaging characteristics across mammography systems, models built using data from one system do not generalize well to other systems. Though federated learning (FL) has emerged as a way to improve the generalizability of AI without the need to share data, the best way to preserve features from all training data during FL is an active area of research. To explore FL methodology, the breast density classification FL challenge was hosted in partnership with the American College of Radiology, Harvard Medical School's Mass General Brigham, University of Colorado, NVIDIA, and the National Institutes of Health National Cancer Institute. Challenge participants were able to submit docker containers capable of implementing FL on three simulated medical facilities, each containing a unique large mammography dataset. The breast density FL challenge ran from June 15 to September 5, 2022, attracting seven finalists from around the world. The winning FL submission reached a linear kappa score of 0.653 on the challenge test data and 0.413 on an external testing dataset, scoring comparably to a model trained on the same data in a central location.<|reference_end|>
@article{schmidt2024fair,
    title={Fair Evaluation of Federated Learning Algorithms for Automated Breast
  Density Classification: The Results of the 2022 ACR-NCI-NVIDIA Federated
  Learning Challenge},
    author={Kendall Schmidt (American College of Radiology, USA), Benjamin Bearce
  (The Massachusetts General Hospital, USA and University of Colorado, USA),
  Ken Chang (The Massachusetts General Hospital), Laura Coombs (American
  College of Radiology, USA), Keyvan Farahani (National Institutes of Health
  National Cancer Institute, USA), Marawan Elbatele (Computer Vision and
  Robotics Institute, University of Girona, Spain), Kaouther Mouhebe (Computer
  Vision and Robotics Institute, University of Girona, Spain), Robert Marti
  (Computer Vision and Robotics Institute, University of Girona, Spain),
  Ruipeng Zhang (Cooperative Medianet Innovation Center, Shanghai Jiao Tong
  University, China and Shanghai AI Laboratory, China), Yao Zhang (Shanghai AI
  Laboratory, China), Yanfeng Wang (Cooperative Medianet Innovation Center,
  Shanghai Jiao Tong University, China and Shanghai AI Laboratory, China),
  Yaojun Hu (Real Doctor AI Research Centre, Zhejiang University, China),
  Haochao Ying (Real Doctor AI Research Centre, Zhejiang University, China and
  School of Public Health, Zhejiang University, China), Yuyang Xu (Real Doctor
  AI Research Centre, Zhejiang University, China and College of Computer
  Science and Technology, Zhejiang University, China), Conrad Testagrose
  (University of North Florida College of Computing Jacksonville, USA), Mutlu
  Demirer (Mayo Clinic Florida Radiology, USA), Vikash Gupta (Mayo Clinic
  Florida Radiology, USA), "Unal Ak"unal (Division of Medical Image
  Computing, German Cancer Research Center, Heidelberg, Germany), Markus
  Bujotzek (Division of Medical Image Computing, German Cancer Research Center,
  Heidelberg, Germany), Klaus H. Maier-Hein (Division of Medical Image
  Computing, German Cancer Research Center, Heidelberg, Germany), Yi Qin
  (Electronic and Computer Engineering, Hong Kong University of Science and
  Technology, China), Xiaomeng Li (Electronic and Computer Engineering, Hong
  Kong University of Science and Technology, China), Jayashree Kalpathy-Cramer
  (The Massachusetts General Hospital, USA and University of Colorado, USA),
  Holger R. Roth (NVIDIA, USA)},
    journal={Medical Image Analysis Volume 95, July 2024, 103206},
    year={2024},
    doi={10.1016/j.media.2024.103206.},
    archivePrefix={arXiv},
    eprint={2405.14900},
    primaryClass={eess.IV cs.CV cs.LG}
}