Vision-Language Transformer for Medical Image Label Mining
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Solution Overview
Problem
The lack of sufficient and accurately labeled training data for machine learning systems, particularly in medical imaging, leads to inaccurate results and failure to converge, necessitating improved methods to increase the availability of training data.
Innovation Solution
A supervised cross-modal vision and language transformer (CAVIAR) network is used to mine annotations from unannotated medical datasets, leveraging natural language supervision and transformer models with noise contrastive estimation, cross-modal feature-wise linear modulation, and recursive textual mining to enhance localization accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional supervised learning methods are used with expert-annotated data, then training accuracy can be achieved, but the cost and time required for data labeling increases significantly
Solution Approach 1:
The system performs preliminary actions by pre-training transformer models on large-scale unannotated medical data before fine-tuning with expert annotations. This preliminary processing of unannotated data creates intermediate representations that reduce the burden of expert labeling while maintaining training accuracy.
Solution Approach 2:
The patent introduces intermediary components including transformer-based language models and vision-language transformers that act as mediators between unannotated data and expert annotations. These intermediaries automatically extract features and generate pseudo-labels from unannotated data, reducing the direct burden on expert annotators.
2Reliability
If more expert-annotated training data is collected, then model performance improves, but the expense and complexity of data acquisition increases
Solution Approach 1:
The system enables self-service by allowing the model to automatically learn from unannotated data through self-supervised learning mechanisms. The transformer models perform automatic feature extraction and representation learning without requiring expert intervention for each data point, reducing acquisition complexity while maintaining performance.
Solution Approach 2:
The training process is segmented into multiple stages: pre-training on unannotated data, fine-tuning on partially annotated data, and final training on expert-annotated data. This segmentation allows the system to leverage different data sources at different stages, reducing the immediate need for large amounts of expert-annotated data while achieving high performance.
3Measurement precision
If transformer models with multiple processing stages are used, then localization accuracy improves by 8.0%, but computational resource requirements increase
Solution Approach 1:
The system performs preliminary feature extraction and representation learning using transformer models on unannotated data before the main training task. This preliminary processing creates optimized feature representations that reduce the computational burden during subsequent fine-tuning and inference stages, making the overall process more energy-efficient despite the complexity of the transformer architecture.
Data Source
AI summary
Apparatuses, systems, and techniques to generate labeled training data. In at least one embodiment, labeled training images are generated from medial images annotated with natural language text.