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A Federated Learning Approach to Tumor Detection in Colon Histology Images

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Abstract

Federated learning (FL), a relatively new area of research in medical image analysis, enables collaborative learning of a federated deep learning model without sharing the data of participating clients. In this paper, we propose FedDropoutAvg, a new federated learning approach for detection of tumor in images of colon tissue slides. The proposed method leverages the power of dropout, a commonly employed scheme to avoid overfitting in neural networks, in both client selection and federated averaging processes. We examine FedDropoutAvg against other FL benchmark algorithms for two different image classification tasks using a publicly available multi-site histopathology image dataset. We train and test the proposed model on a large dataset consisting of 1.2 million image tiles from 21 different sites. For testing the generalization of all models, we select held-out test sets from sites that were not used during training. We show that the proposed approach outperforms other FL methods and reduces the performance gap (to less than 3% in terms of AUC on independent test sites) between FL and a central deep learning model that requires all data to be shared for centralized training, demonstrating the potential of the proposed FedDropoutAvg model to be more generalizable than other state-of-the-art federated models. To the best of our knowledge, ours is the first study to effectively utilize the dropout strategy in a federated setting for tumor detection in histology images.

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Availability of Data and Materials

This research study was conducted retrospectively using human subject data made available in open access by TCGA Research Network (https://www.cancer.gov/tcga). Ethical approval was not required as confirmed by the license attached with the open-access data.

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Funding

This work was partly supported by the Department of Computer Science, University of Warwick, and partly by GSK.

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Authors and Affiliations

  1. The Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, Coventry, UK

    Gozde N. Gunesli, Mohsin Bilal, Shan E Ahmed Raza & Nasir M. Rajpoot

Authors
  1. Gozde N. Gunesli
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  2. Mohsin Bilal
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  3. Shan E Ahmed Raza
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  4. Nasir M. Rajpoot
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Contributions

All authors conceptualized the study. GG was responsible for the drafting and final manuscript preparation, the software, the analysis of the results, and the preparation of figures and tables. NR designed the overall study setting with GG. MB contributed to data preparation. All analyzed, interpreted and helped to communicate the results. All provided significant writing, reviewing, and editing of all versions.

Corresponding authors

Correspondence to Gozde N. Gunesli or Nasir M. Rajpoot.

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Competing Interests

NR is a Director and CSO of Histofy Ltd. NR declares that he is in receipt of research funding from AstraZeneca and GlaxoSmithKline (GSK) and is also a GSK Chair of Computational Pathology at the University of Warwick, UK. All other authors declare no competing interests.

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Gunesli, G., Bilal, M., Raza, S. et al. A Federated Learning Approach to Tumor Detection in Colon Histology Images. J Med Syst 47, 99 (2023). https://doi.org/10.1007/s10916-023-01994-5

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