Federated Self-Supervised Learning for Surgical Data Privacy
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Solution Overview
Problem
Developing machine learning models for surgical data science tasks is hindered by the difficulty in acquiring large and diverse labeled data due to data protection laws and site-specific variations in surgical data distribution across clinical sites, limiting the ability to train effective models that generalize across different environments.
Innovation Solution
A federated self-supervised learning approach where a student and teacher version of the machine learning model are used to generate features from image crops, with the student model trained based on losses computed between these features, and the trained models are aggregated at a server to generate a global model, allowing for training without sharing sensitive data and enabling self-supervised learning with unlabeled data before fine-tuning with labeled data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If surgical data is transferred to a centralized location for training machine learning models, then model training quality improves, but data privacy and security are compromised
Solution Approach 1:
The system segments the centralized training process into distributed local training at multiple clinical sites. Each site trains its own model locally using its data, then only model parameters (not raw data) are shared and aggregated at a central server. This segmentation allows model training quality to improve through multi-site collaboration while maintaining data privacy since sensitive surgical data never leaves its originating site.
Solution Approach 2:
The patent introduces an intermediary mechanism where model parameters serve as the medium for knowledge transfer between clinical sites. Instead of directly sharing sensitive surgical data, the system uses anonymized model parameters as an intermediary that carries training information without exposing patient data. This intermediary approach enables collaborative model training while protecting data privacy.
2Quantity of substance
If manual annotation or labeling of surgical data is performed at each clinical site, then labeled training data is generated, but resource requirements and time consumption increase
Solution Approach 1:
The system performs preliminary self-supervised pre-training of models using unlabeled surgical data from multiple clinical sites before fine-tuning with labeled data. This preliminary action allows the model to learn general surgical patterns from abundant unlabeled data, reducing the amount of time-consuming manual annotation needed later for supervised fine-tuning while still generating high-quality labeled training data when needed.
3Ease of manufacture
If machine learning models are trained on surgical data from one clinical site, then model training is simplified, but model performance deteriorates on data from different clinical sites
Solution Approach 1:
The patent merges models trained at multiple clinical sites through parameter aggregation at a central server. Each site trains its model locally (maintaining training simplicity), then the server combines these distributed models into a unified global model that captures diverse surgical patterns from all sites. This merging process improves model generalization to different clinical sites while preserving the simplicity of local training procedures.
Solution Approach 2:
The system creates a universal global model that functions across multiple clinical sites with different data distributions. The federated learning framework enables a single model to adapt to various site-specific characteristics while maintaining overall universality. This multi-functional model can be deployed at any clinical site and performs well on local data despite being trained on diverse data from multiple sites.
4Quantity of substance
If large volumes of labeled surgical data are collected for model training, then model accuracy improves, but data acquisition difficulty increases due to data protection laws
Solution Approach 1:
The system enables self-service learning where models automatically learn from unlabeled surgical data through self-supervised pre-training. This eliminates the need for extensive manual labeling of large volumes of data, as the model can extract useful patterns directly from unlabeled data. The self-service approach reduces data acquisition complexity while still achieving high model accuracy through the subsequent fine-tuning stage with limited labeled data.
Data Source
AI summary
One embodiment of the present invention sets forth a technique for training a machine learning model to perform feature extraction. The technique includes executing a student version of the machine learning model to generate a first set of features from a first set of image crops and executing a teacher version of the machine learning model to generate a second set of features from a second set of image crops. The technique also includes training the student version of the machine learning model based on one or more losses computed between the first and second sets of features. The technique further includes transmitting the trained student version of the machine learning model to a server, wherein the trained student version can be aggregated by the server with additional trained student versions of the machine learning model to generate a global version of the machine learning model.