Annotation-Efficient Deep Learning Models for Medical Imaging
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Solution Overview
Problem
The challenge in medical imaging is the high cost and time-consuming nature of annotating medical images, which is essential for training deep learning models, especially when dealing with rare diseases and limited data availability.
Innovation Solution
The development of annotation-efficient deep learning models that utilize sparsely-annotated or annotation-free training datasets, incorporating techniques such as active learning, interactive segmentation, few-shot learning, self-supervised learning, and synthetic data augmentation to reduce the reliance on extensive annotated data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional supervised learning with extensive annotated data is used, then model accuracy is improved, but annotation cost and time increase significantly
Solution Approach 1:
The system employs self-supervised learning where the model learns from unannotated medical images by creating and solving its own pretext tasks (e.g., image reconstruction, rotation prediction). This self-service mechanism eliminates the need for extensive manual annotations while still achieving high accuracy in medical image analysis tasks.
Solution Approach 2:
The framework performs preliminary self-supervised pre-training on large volumes of unannotated medical images before fine-tuning on small annotated datasets. This preliminary action of learning general features from unannotated data reduces the subsequent need for extensive annotations, thereby reducing annotation time and cost.
2Measurement precision
If traditional supervised learning with extensive annotated data is used, then model accuracy is improved, but annotation cost increases significantly
Solution Approach 1:
The model performs self-supervised learning by automatically generating supervision signals from unannotated data through pretext tasks. This self-service approach eliminates dependency on expensive expert annotations while maintaining high model accuracy, directly addressing the annotation cost issue.
Solution Approach 2:
The system creates synthetic annotated data through data augmentation and synthesis techniques, generating copies of unannotated images with simulated annotations. This copying mechanism provides training supervision without requiring actual expert annotations, reducing annotation costs while maintaining model performance.
3Loss of time
If sparsely-annotated training is used, then annotation time is reduced, but model performance may deteriorate
Solution Approach 1:
The framework performs preliminary self-supervised pre-training on large volumes of unannotated medical images to learn robust feature representations. This preliminary action equips the model with general medical image understanding, enabling it to achieve high performance even when fine-tuned on sparsely annotated data.
Solution Approach 2:
The system transitions from direct supervised learning to a two-stage process involving self-supervised pre-training followed by supervised fine-tuning. This dimensional change in the learning paradigm allows the model to leverage both unannotated and annotated data effectively, maintaining high performance with reduced annotation requirements.
4Ease of manufacture
If sparsely-annotated training is used, then annotation cost is reduced, but model performance may deteriorate
Solution Approach 1:
The system performs preliminary self-supervised pre-training on unannotated data to learn general features, which serves as a foundation for subsequent fine-tuning on sparsely annotated data. This preliminary action reduces the performance gap that would otherwise result from limited annotations, maintaining high model performance while reducing annotation costs.
Solution Approach 2:
The framework generates synthetic annotated images through data augmentation and synthesis, creating copies of unannotated images with simulated annotations. This copying provides additional training supervision without requiring actual expert annotations, thereby maintaining model performance while reducing real annotation costs.
Data Source
AI summary
Described herein are means for implementing annotation-efficient deep learning models utilizing sparsely-annotated or annotation-free training, in which trained models are then utilized for the processing of medical imaging. An exemplary system includes at least a processor and a memory to execute instructions for learning anatomical embeddings by forcing embeddings learned from multiple modalities; initiating a training sequence of an AI model by learning dense anatomical embeddings from unlabeled data, then deriving application-specific models to diagnose diseases with a small number of examples; executing collaborative learning to generate pretrained multimodal models; training the AI model using zero-shot or few-shot learning; embedding physiological and anatomical knowledge; embedding known physical principles refining the AI model; and outputting a trained AI model for use in diagnosing diseases and abnormal conditions in medical imaging. Other related embodiments are disclosed.