Summary of Fedmeki: a Benchmark For Scaling Medical Foundation Models Via Federated Knowledge Injection, by Jiaqi Wang et al.
FEDMEKI: A Benchmark for Scaling Medical Foundation Models via Federated Knowledge Injection
by Jiaqi Wang, Xiaochen Wang, Lingjuan Lyu, Jinghui Chen, Fenglong Ma
First submitted to arxiv on: 17 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 The Federated Medical Knowledge Injection (FEDMEKI) platform is a novel approach to integrating medical knowledge into foundation models while preserving privacy. By using cross-silo federated learning, FEDMEKI addresses the challenges of centralized data collection prohibited by regulations like HIPAA in the USA. The platform handles multi-site, multi-modal, and multi-task medical data, including 7 modalities and 8 medical tasks such as lung opacity detection, COVID-19 detection, ECG abnormal detection, mortality prediction, sepsis prediction, enlarged cardiomediastinum detection, MedVQA, and ECG noise clarification. The dataset is partitioned across several clients to facilitate decentralized training under 16 benchmark approaches. FEDMEKI preserves data privacy while enhancing the capability of medical foundation models by allowing them to learn from a broader spectrum of medical knowledge without direct data exposure. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FEDMEKI is a new way to combine medical information with artificial intelligence. It lets AI models learn from many different sources of medical data, but keeps that data private and secure. The platform handles lots of different types of medical data, including images, sounds, texts, lab results, vital signs, and more. It uses this data to train AI models on 8 specific medical tasks, like detecting COVID-19 or predicting mortality rates. FEDMEKI is important because it helps keep patient information safe while still allowing AI models to learn from a wide range of medical knowledge. |
Keywords
* Artificial intelligence * Federated learning * Multi modal * Multi task
