Self-Supervised Learning for Medical Imaging Analysis
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Solution Overview
Problem
Artificial intelligence-based systems for medical imaging analysis often perform poorly due to limited training data, which results in limited performance and generalizability, especially given the constraints of privacy regulations and high annotation costs.
Innovation Solution
The implementation of self-supervised learning methods that generate augmented images and optimize encoder networks using contrastive clustering loss functions, allowing for training with unannotated medical images and incorporating federated and continual learning to enhance robustness and scalability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If relatively small sets of training data are used for training artificial intelligence-based systems, then training cost and time are reduced, but performance and generalizability on unseen data deteriorate
Solution Approach 1:
The patent applies self-supervised learning to pre-train encoder networks on large volumes of unannotated medical imaging data before fine-tuning on smaller annotated datasets. This preliminary action of learning from abundant unannotated data establishes robust feature representations that improve generalizability while maintaining training efficiency.
Solution Approach 2:
The system uses self-supervised learning where the model learns to represent medical images by predicting transformations or properties of the images themselves without requiring external annotations. This self-service mechanism allows the system to leverage unannotated data effectively, improving performance without proportionally increasing annotation costs.
2Reliability
If relatively large sets of training data are used for training artificial intelligence-based systems, then performance and generalizability improve, but availability is limited due to privacy regulations and high annotation costs
Solution Approach 1:
The patent extracts and utilizes the abundant unannotated data that would otherwise be unavailable for training. By separating the pre-training phase (using unannotated data) from the fine-tuning phase (using annotated data), the system extracts value from data that was previously unusable, overcoming annotation cost barriers.
Solution Approach 2:
Self-supervised learning acts as an intermediary mechanism that bridges the gap between unannotated and annotated data. It enables the system to process and learn from unannotated data, which then serves as a foundation for subsequent supervised learning on annotated data, effectively expanding available training resources.
3Ease of manufacture
If traditional supervised learning is used with limited annotated data, then training cost is reduced, but the appearance variability of anatomies and abnormalities cannot be fully captured
Solution Approach 1:
The system performs preliminary self-supervised pre-training on diverse unannotated images with varying appearances before supervised fine-tuning. This preliminary exposure to appearance variability in unannotated data enables the model to learn robust features that generalize across different appearances while maintaining cost efficiency.
Data Source
AI summary
Systems and methods for training an artificial intelligence-based system using self-supervised learning are provided. For each respective training medical image of a set of unannotated training medical images, the following steps are performed. A first augmented image is generated by applying a first augmentation operation to the respective training medical image. A second augmented image is generated by applying a second augmentation operation to the respective training medical image. A first representation vector is created from the first augmented image using an encoder network. A second representation vector is created from the second augmented image using the encoder network. The first representation vector is mapped to first cluster codes. The second representation vector is mapped to second cluster codes. The encoder network is optimized using the first and second representation vectors and the first and second cluster codes.