Collaborative training of medical artificial intelligence models with non-uniform labels

Scientific Reports, vol. 13, pp. 6046

Abstract

Due to the rapid advancements in recent years, medical image analysis is largely dominated by deep learning (DL). However, building powerful and robust DL models requires training with large multi-party datasets. While multiple stakeholders have provided publicly available datasets, the ways in which these data are labeled vary widely. For Instance, an institution might provide a dataset of chest radiographs containing labels denoting the presence of pneumonia, while another institution might have a focus on determining the presence of metastases in the lung. Training a single AI model utilizing all these data is not feasible with conventional federated learning (FL). This prompts us to propose an extension to the widespread FL process, namely flexible federated learning (FFL) for collaborative training on such data. Using 695,000 chest radiographs from five institutions from across the globe-each with differing labels-we demonstrate that having heterogeneously labeled datasets, FFL-based training leads to significant performance increase compared to conventional FL training, where only the uniformly annotated images are utilized. We believe that our proposed algorithm could accelerate the process of bringing collaborative training methods from research and simulation phase to the real-world applications in healthcare.

Authors 9

1. Soroosh Tayebi Arasteh Aachen Department of Diagnostic and Interventional Radiology

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Pauwelsstr. 30, 52074, Aachen, Germany

   Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany

2. Peter Isfort Aachen Department of Diagnostic and Interventional Radiology

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Pauwelsstr. 30, 52074, Aachen, Germany

   Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany

3. Marwin Saehn Aachen Department of Diagnostic and Interventional Radiology

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Pauwelsstr. 30, 52074, Aachen, Germany

   Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany

4. Gustav Mueller-Franzes Aachen Department of Diagnostic and Interventional Radiology

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Pauwelsstr. 30, 52074, Aachen, Germany

   Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany

5. Firas Khader Aachen Department of Diagnostic and Interventional Radiology

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Pauwelsstr. 30, 52074, Aachen, Germany

   Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany

6. Jakob Nikolas Kather Aachen Department of Medicine III

   University of Leeds · University Hospital Heidelberg · National Center for Tumor Diseases · Universitätsklinikum Aachen · Else Kröner Fresenius Center for Digital Health · Technische Universität Dresden · RWTH Aachen University

   Affiliation as printed

   Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany

   Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK

   Medical Faculty Carl Gustav Carus, Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany

   Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany

   Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James’s, University of Leeds, Leeds, UK

7. Christiane Katharina Kuhl Aachen Department of Diagnostic and Interventional Radiology

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Pauwelsstr. 30, 52074, Aachen, Germany

   Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany

8. Sven Nebelung Aachen Department of Diagnostic and Interventional Radiology

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Pauwelsstr. 30, 52074, Aachen, Germany

   Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany

9. Daniel Truhn corresponding Aachen Department of Diagnostic and Interventional Radiology

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Pauwelsstr. 30, 52074, Aachen, Germany. dtruhn@ukaachen.de

   Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany