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Active Learning Based on Temporal Difference of Gradient Flow in Thoracic Disease Diagnosis
IEEE Journal of Biomedical and Health Informatics
|March 24, 2025
Summary
This study introduces a new active learning method using Temporal Difference of Gradient Flow (TDGF) to efficiently select uncertain samples for thoracic disease diagnosis, reducing annotation costs and improving model performance.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Machine Learning
Background:
- Deep learning significantly advances thoracic disease diagnosis but requires extensive annotated data, which is costly and time-consuming to acquire.
- Active learning strategies aim to reduce annotation burden by strategically selecting informative samples for labeling.
- Current active learning methods often neglect sample impact on model training dynamics and incur high selection costs.
Purpose of the Study:
- To propose a novel metric, Temporal Difference of Gradient Flow (TDGF), for efficient and effective data selection in active learning for thoracic disease diagnosis.
- To address the limitations of existing active learning methods, specifically their oversight of sample influence on model training and high computational costs.
Main Methods:
- A three-step active learning cycle: model training, data selection using TDGF, and data annotation.
- Training a target model, a proxy model, and a historical proxy model on the labeled dataset.
- Calculating TDGF scores based on surrogate gradient flow between proxy models to identify and select the most uncertain unlabeled samples.
Main Results:
- The TDGF metric effectively identifies hard and uncertain samples crucial for model training.
- Employing proxy models and surrogate gradient flow significantly reduces the computational complexity of data selection.
- The proposed TDGF-based active learning method demonstrates superior performance compared to existing methods on public chest radiograph datasets (ChestX-ray14, CheXpert).
Conclusions:
- The TDGF metric offers an efficient and effective approach for data selection in active learning for medical image analysis.
- This method enhances the performance of deep learning models in thoracic disease diagnosis while minimizing annotation efforts.
- TDGF represents a promising advancement in active learning for resource-constrained medical AI applications.
