Enhancing robustness and generalization in microbiological few-shot detection through synthetic data generation and contrastive learning

In many medical and pharmaceutical processes, continuous hygiene monitoring is crucial, often involving the manual detection of microorganisms in agar dishes by qualified personnel. Although deep learning methods hold promise for automating this task, they frequently encounter a shortage of sufficient training data, a prevalent challenge in colony detection. To overcome this limitation, we propose a novel pipeline that combines generative data augmentation with few-shot detection. Our approach aims to significantly enhance detection performance, even with (very) limited training data. A main component of our method is a diffusion-based generator model that inpaints synthetic bacterial colonies onto real agar plate backgrounds. This data augmentation technique enhances the diversity of training data, allowing for effective model training with only 25 real images. Our method outperforms common training-techniques, demonstrating a +0.45 mAP improvement compared to training from scratch, and a +0.15 mAP advantage over the current SOTA in synthetic data augmentation. Additionally, we integrate a decoupled feature classification strategy, where class-agnostic detection is followed by lightweight classification via a feed-forward network, making it possible to detect and classify colonies with minimal examples. This approach achieves an AP⁵⁰ score of 0.7 in a few-shot scenario on the AGAR dataset. Our method also demonstrates robustness to various image corruptions, such as noise and blur, proving its applicability in real-world scenarios. By reducing the need for large labeled datasets, our pipeline offers a scalable, efficient solution for colony detection in hygiene monitoring and biomedical research, with potential for broader applications in fields where rapid detection of new colony types is required.