Self-Supervised Medical Feature Extraction for Data Anonymization
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Solution Overview
Problem
Existing methods for training machine learning models (MLMs) in the medical field face challenges such as the need for large volumes of labeled data, high computational costs, and difficulties in generalizing model performance due to limited training data and varying data distributions.
Innovation Solution
A method involving unsupervised or self-supervised training of a fundamental machine learning model (fMLM) with a feature extraction module (xMLM) is proposed. This model is trained on medical data and can be used to extract relevant features, which are then stored for later use in anonymizing medical data and training other models for processing and analyzing medical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If supervised training with large volumes of labeled data is used, then model performance and generalizability are improved, but data acquisition cost and time increase significantly
Solution Approach 1:
The system performs preliminary unsupervised pre-training on large volumes of unlabeled medical data to initialize the model with domain-specific knowledge before fine-tuning with smaller labeled datasets. This preliminary action embeds medical image characteristics, anatomical structures, and imaging modalities into the model, reducing the need for extensive labeled data later
Solution Approach 2:
The patent introduces self-supervised learning tasks (such as image reconstruction, rotation prediction, or contrastive learning) as intermediary training objectives that bridge the gap between unlabeled and labeled data. These intermediary tasks enable the model to learn meaningful representations without requiring explicit annotations
2Measurement precision
If deep neural networks with millions of parameters are trained, then feature extraction capability is improved, but computational cost and training time increase
Solution Approach 1:
The system performs preliminary training on unlabeled data to establish strong feature extraction capabilities before fine-tuning with labeled data. This preliminary phase allows the use of computationally intensive architectures without proportionally increasing the cost of labeled data training
Solution Approach 2:
The patent employs parameter-efficient fine-tuning techniques where only a subset of parameters (such as adapter layers, LoRA weights, or final classification layers) are updated during supervised training, while the majority of pre-trained parameters remain frozen. This dramatically reduces computational cost while maintaining feature extraction performance
3Reliability
If medical data is used for training models, then model accuracy is improved, but data privacy and security requirements increase
Solution Approach 1:
The system extracts only the essential feature representations from medical data while discarding or anonymizing personally identifiable information. The extracted features capture medical relevance (anatomical structures, pathological patterns) without containing patient identity, enabling training with reduced privacy risk
Solution Approach 2:
The patent introduces intermediate representation layers that transform raw medical data into anonymized feature embeddings before further processing. These intermediate representations serve as a privacy-preserving intermediary, allowing model training on medical data while preventing direct access to sensitive patient information
Data Source
AI summary
A fundamental machine learning model, fMLM, is provisioned in the untrained or in a partially trained state to provide a trained machine learning model for feature extraction, xMLM, from medical data, wherein the fMLM has an architecture that is trainable by way of unsupervised or self-supervised training. The fMLM has the xMLM and at least one downstream machine learning model, nMLM, for performing at least one corresponding downstream task. First medical data is obtained and the fMLM is trained in an unsupervised or a self-supervised manner based on the first medical data. The xMLM is taken from trained fMLM and stored.