Multi-center Cancer Prognosis Prediction via Migration Learning
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Solution Overview
Problem
Current cancer prognosis prediction models face limitations due to insufficient labeled data in single institutions, poor generalization ability across heterogeneous data sets, and risks of patient privacy leakage during multi-institutional data sharing.
Innovation Solution
A multi-center synergetic cancer prognosis prediction system utilizing multi-source migration learning, which includes a model parameter setting module, data screening module, and multi-source migration learning module to preprocess and integrate data from multiple centers, calculate migration weights, and train accurate prediction models while maintaining patient privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If multi-institutional data is aggregated to train a general model, then the sample size increases, but the generalization ability deteriorates due to data heterogeneity
Solution Approach 1:
The patent segments the multi-institutional data into multiple data centers, each maintaining its data locally. Instead of aggregating all data into a single training set, the system divides the training process across multiple centers, with each center contributing to the model training through distributed computation. This segmentation preserves data heterogeneity while enabling collaborative model improvement.
Solution Approach 2:
The patent applies local quality by allowing each data center to maintain its own data characteristics and distribution patterns. The system does not homogenize the data from different institutions but rather respects the local data quality and distribution at each center. This approach enables the model to adapt to local data characteristics while still benefiting from multi-center collaboration.
2Measurement precision
If local labeled samples are used to calibrate the general model, then the model performance improves, but the requirement for labeled samples increases
Solution Approach 1:
The patent enables each data center to perform self-service model calibration using its own local labeled samples. The system allows local centers to independently adjust and optimize the model parameters using their own data without requiring extensive centralized labeled data. This self-service approach reduces the dependency on large amounts of labeled samples while maintaining model performance.
3Measurement precision
If multiple institutions share data for model training, then the model accuracy improves, but the risk of patient privacy leakage increases
Solution Approach 1:
The patent introduces a federated learning intermediary layer that enables collaboration between multiple institutions without direct data sharing. The system uses a central server as an intermediary to coordinate model training, where only model parameters and gradients are exchanged, not the actual patient data. This intermediary mechanism preserves patient privacy while still enabling multi-institutional model improvement.
Data Source
AI summary
Provided is a multi-center synergetic cancer prognosis prediction system based on multi-source migration learning. The system includes a model parameter setting module, a data screening module, and a multi-source migration learning module, wherein the model parameter setting module is responsible for setting cancer prognosis prediction model parameters; the data screening module is arranged at a clinical center, and a management center transmits the set model parameter to each clinical center, such that each clinical center inquires a sample feature and prognosis index data from a local database according to the model parameter, so as to preprocess the data; and the multi-source migration learning module includes a source model training unit, a migration weight calculation unit, and a target model calculation unit.