MetaPill is a novel meta-learning framework for extreme few-shot pill detection that enables single-example adaptation to novel medication classes. It integrates a hybrid architecture combining MAML with Prototypical Networks, augmented by domain-specific visual feature extraction and contrastive learning objectives. The framework addresses challenges in pharmaceutical image recognition, including visual similarity, domain shift, and catastrophic forgetting. Validated through comprehensive experiments on established pill recognition benchmarks, MetaPill demonstrates state-of-the-art performance in 1-shot and 5-shot scenarios.
Key findings
MetaPill combines MAML and Prototypical Networks for fine-grained pill recognition.
A novel prototype refinement mechanism using feature space transformations and contrastive learning improves discriminability.
Cross-domain adaptation strategies bridge the gap between controlled training and real-world conditions.
Comprehensive evaluation on pill recognition benchmarks shows state-of-the-art performance in few-shot scenarios.
Limitations & open questions
The framework's scalability for a very large number of medication classes is yet to be tested.
Real-world deployment under diverse conditions requires further extensive testing.