arXiv (All)
open access
Federated Learning Framework for Privacy-Preserving Kidney Stone Detection
Recent innovations in deep learning have significantly enhanced the diagnosis of medical images, although they are based on the use of centralized data storage that pose severe threats to patient privacy and medical data security. To address this issue, this research proposes a Federated Learning (F
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Federated Learning Models for Privacy-Preserving Medical Image Analysis#215781
- Faithful Faithfulness Evaluations: Challenges & Pitfalls Learned from a Breast MRI Case Study#1061883
- Federated learning and differential privacy for medical image analysis#177282
- Federated Learning for Surgical Vision in Appendicitis Classification: Results of the FedSurg EndoVis 2024 Challenge#998029
- Medical Image Classification with Machine Learning Classifier#89267
Record · ID 1014328
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive