TrCL-AGS: A Universal Sequential Triple-Stage Contrastive Learning Framework for Bacterial Detection With Across-Growth-Stage Information

The imprudent use of antibiotics has led to increased bacterial resistance, posing a serious threat to public health. Traditional visual-based antibiotic susceptibility testing (AST) methods are time-consuming and labor-intensive. Deep-learning-based bacterial detection algorithms have significantly improved the efficiency and accuracy of AST. However, existing deep-learning-based bacterial detection algorithms ignore unique challenges of AST images, such as confluent clusters, complex backgrounds, and morphologically similar noise. To address these issues, we propose a novel bacterial detection framework named the triple-stage contrastive learning framework with across-growth-stage information (TrCL-AGS). Specifically, we design a novel across-growth-stage pretraining (AGSP) task to mine across-growth-stage information from the continuous images, providing a richer and more robust feature space for subsequent stages. Instance-level contrastive learning (ILCL) is constructed to extract invariant semantic features of bacteria at a particular growth stage and exclude features similar to bacteria but do not match the specific growth stages. Besides, discriminative features between objects and backgrounds are enhanced by the category-level contrastive learning (CLCL) stage. Extensive experiments on a real-world clinical bacterial AST dataset demonstrate that our method can achieve state-of-the-art performance compared with existing object detection methods. And our framework is compatible with most detectors.