Federated Learning for Digital Pathology Without Patient Data Sharing
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Solution Overview
Problem
The challenge of training machine learning models for digital pathology image analysis is hindered by privacy concerns and the difficulty in obtaining large quantities of patient data, leading to inaccurate tumor detection and classification due to the need for manual annotations and pathologist bias.
Innovation Solution
A federated learning system that distributes a global model to client devices for local training without sharing patient data, using convolutional neural networks for image segmentation and classification, and updates the model iteratively based on user inputs and performance validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If patient data is collected and shared for training machine learning models, then model accuracy improves, but patient privacy is compromised
Solution Approach 1:
The system segments the training process into local model training at each institution and centralized aggregation at the server. Patient data remains segmented and localized at each institution, never leaving the facility, while model parameters are aggregated centrally to improve overall model accuracy without compromising patient privacy.
Solution Approach 2:
The federated learning server acts as an intermediary that coordinates model training without directly accessing patient data. The server aggregates model updates from multiple institutions using techniques like federated averaging, enabling collaborative model improvement while patient data remains isolated at local institutions.
2Measurement precision
If manual annotations are used for training data, then ground truth is established, but pathologist bias and errors are introduced
Solution Approach 1:
The system implements feedback loops where model predictions are continuously refined based on performance validation. Multiple institutions' data collectively train the model, providing diverse perspectives that reduce individual pathologist bias. The feedback from various local datasets helps correct errors and improve consistency across different annotation styles.
Solution Approach 2:
The system merges training data from multiple institutions through federated learning, combining diverse datasets with different annotation styles and pathologist perspectives. This aggregation of multiple sources reduces individual bias and errors while maintaining the benefits of manual annotations for establishing ground truth.
3Quantity of substance
If large quantities of patient data are obtained, then model training quality improves, but data acquisition difficulty increases due to privacy concerns
Solution Approach 1:
The system transitions from collecting large volumes of raw patient data to collecting and processing model parameters in a different dimensional space. Instead of aggregating patient data centrally, the federated learning approach aggregates model updates, effectively changing the dimension of collaboration from data space to parameter space, thereby enabling large-scale training without data sharing.
Solution Approach 2:
The system creates and distributes copies of the global model to multiple institutions for local training, then aggregates the learned parameters back to update the global model. This copying mechanism enables parallel training across institutions with large local datasets without requiring central data collection, overcoming data acquisition barriers while maintaining model quality.
4Device complexity
If centralized data collection is implemented, then model training is simplified, but data privacy and security requirements increase
Solution Approach 1:
The system inverts the traditional centralized training approach by distributing model training to local institutions while maintaining centralized coordination. Instead of collecting data centrally and training one model, the global model is distributed to multiple locations for parallel training, with results aggregated centrally. This inversion reduces data security risks while maintaining training effectiveness.
Data Source
AI summary
A method for using a federated learning classifier in digital pathology includes distributing, by a centralized server, a global model to a plurality of client devices. The client devices further train the global model using a plurality images of a specimen and corresponding annotations to generate at least one further trained model. The client devices provide further trained models to the centralized server, which aggregates the further trained models with the global model to generate an updated global model. The updated global model is then distributed to the plurality of client devices.