Summary of Privacy Preserving Federated Learning in Medical Imaging with Uncertainty Estimation, by Nikolas Koutsoubis et al.
Privacy Preserving Federated Learning in Medical Imaging with Uncertainty Estimation
by Nikolas Koutsoubis, Yasin Yilmaz, Ravi P. Ramachandran, Matthew Schabath, Ghulam Rasool
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Image and Video Processing (eess.IV); Machine Learning (stat.ML)
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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 Machine learning (ML) models have great potential in healthcare, particularly in medical imaging, where they can improve disease diagnoses, treatment planning, and post-treatment monitoring. Various computer vision tasks are poised to become routine in clinical analysis, but there’s a hurdle: privacy concerns surrounding patient data make it difficult to assemble large training datasets. Federated Learning (FL) emerges as a solution, enabling organizations to collaborate on ML model training by sharing gradients rather than data. FL facilitates inter-institutional collaboration while preserving patient privacy. However, FL faces challenges like sensitive information being gleaned from shared gradients and accurately quantifying model confidence/uncertainty in noisy medical imaging data. This paper offers a comprehensive review of FL, privacy preservation, and uncertainty estimation, with a focus on medical imaging. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Medical imaging uses machine learning (ML) to improve disease diagnoses, treatment planning, and post-treatment monitoring. But there’s a problem: patient data is private! To solve this, researchers use something called Federated Learning (FL). FL lets organizations work together on ML models without sharing patient data. This helps keep patients’ info safe. However, FL has some challenges too, like making sure the model is confident and accurate in noisy medical imaging data. |
Keywords
» Artificial intelligence » Federated learning » Machine learning
