2025Vivas iceFinder
Citation
A. Vivas-Lago, D. Castaño-Díez, Few-shot learning for non-vitrified ice segmentation, Scientific Reports, 15, 1, 5501, (2025).
Abstract
This study introduces Ice Finder, a novel tool for quantifying crystalline ice in cryo-electron tomography, addressing a critical gap in existing methodologies. We present the first application of the meta-learning paradigm to this field, demonstrating that diverse tomographic tasks across datasets can be unified under a single meta-learning framework. By leveraging few-shot learning, our approach enhances domain generalization and adaptability to domain shifts, enabling rapid adaptation to new datasets with minimal examples. Ice Finder's performance is evaluated on a comprehensive set of in situ datasets from EMPIAR, showcasing its ease of use, fast processing capabilities, and millisecond inference times.
Keywords
cryo-electron tomography, non-vitrified ice segmentation, crystalline ice, few-shot learning, meta-learning, Ice Finder,