Active Learning based on Temporal Difference of Gradient Flow in Thoracic Disease Diagnosis

Given the significant advancements in thoracic disease diagnosis due to deep learning, there is a reliance on the availability of numerous annotated samples, which, however, can hardly be guaranteed due to the resource-intensive nature of medical image annotation. Active learning has been introduced to mitigate annotation costs by selecting a subset of uncertain samples for annotation and training. Existing active learning methods encounter two primary challenges: (1) overlooking the impact of samples on the dynamics of model training during data selection, and (2) suffering from high costs of data evaluation and selection. To tackle both issues, we propose a novel metric called Temporal Difference of Gradient Flow (TDGF) for data selection in active learning. Each round of active learning involves three steps: model training, data selection, and data annotation. First, we train a target model, a proxy model, and a historical proxy model on the labeled set. Second, the TDGF scores of unlabeled samples are evaluated based on the surrogate gradient flow, i.e., the TDGF w.r.t the final fully-connected layer between the proxy and historical proxy models, and top-K samples with the highest TDGF scores are selected. Third, the selected samples are annotated, and the labeled pool and unlabeled pool are updated. Comparative experiments have been conducted on two public chest radiograph datasets, i.e., ChestX-ray14 and CheXpert. Our results suggest that the proposed TDGF metric is prone to selecting hard and uncertain samples, and the use of proxy models and surrogate gradient flow substantially reduces the complexity of TDGF calculation. More importantly, the results also indicate that our TDGF-based method outperforms classical and state-of-the-art active learning methods in thoracic disease diagnosis.