Medical report generation based on multimodal federated learning.

Area of Science:

• Artificial Intelligence
• Medical Informatics
• Machine Learning

Background:

• Medical image reports are crucial for clinical decisions.
• Sharing medical data for analysis is hindered by privacy concerns.

Purpose of the Study:

• To propose a multimodal federated learning methodology for privacy-preserving medical image reporting.
• To enhance the accuracy and quality of medical image reports while protecting patient confidentiality.

Main Methods:

• Developed a multimodal federated learning architecture for distributed model training.
• Utilized deep learning for multimodal data analysis and report generation.
• Applied federated averaging and an evidence-based optimization algorithm for parameter aggregation.

Main Results:

• Experimental results validate the effectiveness of the proposed approach.
• The method significantly improves patient confidentiality protection compared to centralized methods.
• Achieved enhanced accuracy and overall quality in generated medical image reports.

Conclusions:

• The multimodal federated learning approach offers a novel, privacy-secured solution for medical image reporting.
• This method promises more precise, efficient, and secure medical services.
• It is expected to play a key role in medical image report generation and other healthcare applications.