Summary of Multi-modal Federated Learning For Cancer Staging Over Non-iid Datasets with Unbalanced Modalities, by Kasra Borazjani and Naji Khosravan and Leslie Ying and Seyyedali Hosseinalipour
Multi-Modal Federated Learning for Cancer Staging over Non-IID Datasets with Unbalanced Modalities
by Kasra Borazjani, Naji Khosravan, Leslie Ying, Seyyedali Hosseinalipour
First submitted to arxiv on: 7 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 abstract presents a machine learning-based approach for cancer staging through medical image analysis, utilizing federated learning (FL) to address privacy concerns. By incorporating FL into a multi-modal learning framework, the authors aim to overcome data exposure issues and improve cancer staging accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses machine learning to help doctors stage cancer more accurately by analyzing medical images. It also solves a big problem with patient data being kept private. The researchers are combining different types of data from patient records to make this work. |
Keywords
* Artificial intelligence * Federated learning * Machine learning * Multi modal
