arXiv (All)
open access
OmniMed-FL: A Robust Multimodal Federated Learning Framework for Clinical Diagnosis
Simultaneous assessment of medical imaging and patient records is often required in clinical diagnosis. However, standard machine learning algorithms cannot analyze these data types together. Meanwhile, compliance with HIPAA and GDPR can constrain centralized aggregation of sensitive patient data. T
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 LoRA Adaptation of BiomedCLIP Across Four International Chest X-Ray Cohorts#822503
- Federated Learning Framework for Privacy-Preserving Kidney Stone Detection#1014328
- Machine learning-based diagnostic models for prevalent metabolic syndrome among patients with disuse muscle atrophy: a comparative study of seven algorithms.#465300
- STEP: A Modular Silent Trial Engine for Operational Evaluation of Digital Pathology AI in Routine Workflow#792956
- STEP: A Modular Silent Trial Engine for Operational Evaluation of Digital Pathology AI in Routine Workflow#788952
Record · ID 997254
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