Summary of Advancing Oncology with Federated Learning: Transcending Boundaries in Breast, Lung, and Prostate Cancer. a Systematic Review, by Anshu Ankolekar et al.
Advancing oncology with federated learning: transcending boundaries in breast, lung, and prostate cancer. A systematic review
by Anshu Ankolekar, Sebastian Boie, Maryam Abdollahyan, Emanuela Gadaleta, Seyed Alireza Hasheminasab, Guang Yang, Charles Beauville, Nikolaos Dikaios, George Anthony Kastis, Michael Bussmann, Sara Khalid, Hagen Kruger, Philippe Lambin, Giorgos Papanastasiou
First submitted to arxiv on: 8 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Federated Learning (FL) has emerged as a promising solution to address the limitations of centralized machine learning (ML) in oncology. This systematic review synthesizes current knowledge on state-of-the-art FL in oncology, focusing on breast, lung, and prostate cancer. The comprehensive review evaluates the real-world implementation and impact of FL on cancer care, demonstrating its effectiveness in enhancing ML generalizability, performance, and data privacy in clinical settings. We evaluated advances in FL, showing its growing adoption amid tightening data privacy regulations. FL outperformed centralized ML in 15 out of 25 studies reviewed, spanning diverse ML models and clinical applications, facilitating integration of multi-modal information for precision medicine. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated Learning (FL) is a way to do machine learning that helps with cancer research. It’s special because it can use data from many different places without sharing personal information. This review looks at what FL has accomplished in the field of oncology, focusing on breast, lung, and prostate cancer. The results show that FL can make machine learning models better and more private, which is important for using real-world data to help patients. |
Keywords
» Artificial intelligence » Federated learning » Machine learning » Multi modal » Precision
