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Current approaches require large annotations and exhibit poor generalization to new protein targets. We present CryoFSL (Cryo-EM Few Shot-Learning), a novel few-shot learning framework built on Segment Anything Model 2 with lightweight adapters, enabling robust particle picking with as few as five labeled micrographs and significantly reducing the annotation burden. The framework\u2019s hierarchical adapter design supports dynamic feature modulation for low-SNR and heterogeneous conditions, resolving the trade-off between annotation burden and performance. CryoFSL surpasses both traditional template-based methods and state-of-the-art deep learning models across diverse proteins in the few-shot learning setting, achieving superior recall, precision, and 3D reconstruction resolution with minimal supervision. It maintains stability across heterogeneous micrographs and consistently detects high-quality particles with fewer false-positives. Notably, CryoFSL achieves competitive resolution in density map reconstruction with just a fraction of the particles picked by other methods, redefining efficiency and quality in cryo-EM analysis. This work paves the way for scalable, generalizable, and annotation-efficient particle-picking pipelines. The code is available at https:\/\/github.com\/biplabpoudel25\/CryoFSL.<\/jats:p>","DOI":"10.1093\/bib\/bbag285","type":"journal-article","created":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T11:46:21Z","timestamp":1778845581000},"source":"Crossref","is-referenced-by-count":1,"title":["CryoFSL: an annotation-efficient, few-shot learning framework for robust protein particle picking in cryo-electron microscopy micrographs"],"prefix":"10.1093","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-8636-1449","authenticated-orcid":false,"given":"Biplab","family":"Poudel","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, NextGen Precision Health, University of Missouri , 416 South 6th Street, Columbia, MO 65211 ,","place":["United States"]},{"name":"Bond Life Sciences Center, University of Missouri , 1201 Rollins Street, Columbia, MO 65211 ,","place":["United States"]},{"name":"Department of Applied Computing, Lander 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