Federated Learning Signal Processing for Medical Imaging
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Solution Overview
Problem
Medical imaging techniques such as MRI, CT, and PET face challenges in reducing interference signals during data acquisition, which degrades image quality and accuracy.
Innovation Solution
A system comprising a central server and client devices that maintain and update global and local prediction models for signal processing, using machine learning to identify and separate interference signals from imaging signals, allowing for improved image quality through federated learning and prediction model updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional signal acquisition methods are used in medical imaging, then the imaging process is simple, but interference signals degrade image quality and accuracy
Solution Approach 1:
The patent extracts and separates interference signals from imaging signals using prediction models. The system identifies and removes harmful interference components while preserving the useful imaging data, thereby improving image quality without requiring complex physical shielding
Solution Approach 2:
The patent introduces prediction models as intermediary components that mediate between the raw signal acquisition and final image reconstruction. These models predict and filter interference signals, acting as a computational barrier that protects the imaging process from harmful interference without physical shielding
2Measurement precision
If federated learning is used to update prediction models, then model accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the learning system into multiple independent client devices, each maintaining its own local prediction model. This distributed architecture allows parallel model training and updating without requiring a centralized complex system, improving scalability while managing complexity through modular design
Solution Approach 2:
The patent creates a universal federated learning framework that can be applied across different medical imaging devices and settings. The standardized model update protocol and centralized coordination mechanism provide a multi-functional solution that works across diverse client devices, reducing overall system complexity through standardization
3Measurement precision
If local prediction models are updated continuously, then signal processing accuracy improves, but computational resources are consumed
Solution Approach 1:
The patent implements partial model updates where only certain components of the prediction models are refined based on available data quality and processing needs. This selective updating approach achieves sufficient interference signal reduction without the excessive computational cost of continuous full-model retraining
Solution Approach 2:
The patent incorporates feedback mechanisms where model update decisions are based on performance metrics and data quality assessments. The system adjusts the frequency and extent of model updates based on actual processing needs, optimizing the balance between accuracy improvement and computational energy consumption
Data Source
AI summary
The present disclosure provides methods and systems for federated learning. The systems include a central server and client devices communicatively connected with the central server. The central server may be configured to maintain a global prediction model for signal prediction, and each of the client devices is configured to maintain a local prediction model for signal prediction corresponding to at least one medical device. The client devices include one or more target client devices.