A Review of Medical Federated Learning: Applications in Oncology and Cancer Research
<jats:title>Abstract</jats:title><jats:p>Machine learning has revolutionized every facet of human life, while also becoming more accessible and ubiquitous. Its prevalence has had a powerful impact in healthcare, with numerous applications and intelligent systems achieving clinical...
| Main Authors: | , , , , |
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| Format: | Book |
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Springer International Publishing
2022
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| Online Access: | https://hdl.handle.net/1721.1/144280 |
| _version_ | 1826213388262309888 |
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| author | Chowdhury, Alexander Kassem, Hasan Padoy, Nicolas Umeton, Renato Karargyris, Alexandros |
| author2 | Massachusetts Institute of Technology. Department of Biological Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Biological Engineering Chowdhury, Alexander Kassem, Hasan Padoy, Nicolas Umeton, Renato Karargyris, Alexandros |
| author_sort | Chowdhury, Alexander |
| collection | MIT |
| description | <jats:title>Abstract</jats:title><jats:p>Machine learning has revolutionized every facet of human life, while also becoming more accessible and ubiquitous. Its prevalence has had a powerful impact in healthcare, with numerous applications and intelligent systems achieving clinical level expertise. However, building robust and generalizable systems relies on training algorithms in a centralized fashion using large, heterogeneous datasets. In medicine, these datasets are time consuming to annotate and difficult to collect centrally due to privacy concerns. Recently, Federated Learning has been proposed as a distributed learning technique to alleviate many of these privacy concerns by providing a decentralized training paradigm for models using large, distributed data. This new approach has become the defacto way of building machine learning models in multiple industries (e.g. edge computing, smartphones). Due to its strong potential, Federated Learning is also becoming a popular training method in healthcare, where patient privacy is of paramount concern. In this paper we performed an extensive literature review to identify state-of-the-art Federated Learning applications for cancer research and clinical oncology analysis. Our objective is to provide readers with an overview of the evolving Federated Learning landscape, with a focus on applications and algorithms in oncology space. Moreover, we hope that this review will help readers to identify potential needs and future directions for research and development.</jats:p> |
| first_indexed | 2024-09-23T15:48:37Z |
| format | Book |
| id | mit-1721.1/144280 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T15:48:37Z |
| publishDate | 2022 |
| publisher | Springer International Publishing |
| record_format | dspace |
| spelling | mit-1721.1/1442802023-06-12T17:51:12Z A Review of Medical Federated Learning: Applications in Oncology and Cancer Research Chowdhury, Alexander Kassem, Hasan Padoy, Nicolas Umeton, Renato Karargyris, Alexandros Massachusetts Institute of Technology. Department of Biological Engineering Massachusetts Institute of Technology. Department of Mechanical Engineering <jats:title>Abstract</jats:title><jats:p>Machine learning has revolutionized every facet of human life, while also becoming more accessible and ubiquitous. Its prevalence has had a powerful impact in healthcare, with numerous applications and intelligent systems achieving clinical level expertise. However, building robust and generalizable systems relies on training algorithms in a centralized fashion using large, heterogeneous datasets. In medicine, these datasets are time consuming to annotate and difficult to collect centrally due to privacy concerns. Recently, Federated Learning has been proposed as a distributed learning technique to alleviate many of these privacy concerns by providing a decentralized training paradigm for models using large, distributed data. This new approach has become the defacto way of building machine learning models in multiple industries (e.g. edge computing, smartphones). Due to its strong potential, Federated Learning is also becoming a popular training method in healthcare, where patient privacy is of paramount concern. In this paper we performed an extensive literature review to identify state-of-the-art Federated Learning applications for cancer research and clinical oncology analysis. Our objective is to provide readers with an overview of the evolving Federated Learning landscape, with a focus on applications and algorithms in oncology space. Moreover, we hope that this review will help readers to identify potential needs and future directions for research and development.</jats:p> 2022-08-09T15:23:05Z 2022-08-09T15:23:05Z 2022-07-22 Book http://purl.org/eprint/type/JournalArticle 9783031089985 9783031089992 0302-9743 1611-3349 https://hdl.handle.net/1721.1/144280 Chowdhury, Alexander, Kassem, Hasan, Padoy, Nicolas, Umeton, Renato and Karargyris, Alexandros. 2022. "A Review of Medical Federated Learning: Applications in Oncology and Cancer Research." 10.1007/978-3-031-08999-2_1 10.1007/978-3-031-08999-2_1 Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf Springer International Publishing Renato Umeton |
| spellingShingle | Chowdhury, Alexander Kassem, Hasan Padoy, Nicolas Umeton, Renato Karargyris, Alexandros A Review of Medical Federated Learning: Applications in Oncology and Cancer Research |
| title | A Review of Medical Federated Learning: Applications in Oncology and Cancer Research |
| title_full | A Review of Medical Federated Learning: Applications in Oncology and Cancer Research |
| title_fullStr | A Review of Medical Federated Learning: Applications in Oncology and Cancer Research |
| title_full_unstemmed | A Review of Medical Federated Learning: Applications in Oncology and Cancer Research |
| title_short | A Review of Medical Federated Learning: Applications in Oncology and Cancer Research |
| title_sort | review of medical federated learning applications in oncology and cancer research |
| url | https://hdl.handle.net/1721.1/144280 |
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