{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T03:40:56Z","timestamp":1768794056508,"version":"3.49.0"},"reference-count":43,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2024,12,10]],"date-time":"2024-12-10T00:00:00Z","timestamp":1733788800000},"content-version":"vor","delay-in-days":18,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61932018"],"award-info":[{"award-number":["61932018"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021YFF0704300"],"award-info":[{"award-number":["2021YFF0704300"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,11,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Automatic single particle picking is a critical step in the data processing pipeline of cryo-electron microscopy structure reconstruction. In recent years, several deep learning-based algorithms have been developed, demonstrating their potential to solve this challenge. However, current methods highly depend on manually labeled training data, which is labor-intensive and prone to biases especially for high-noise and low-contrast micrographs, resulting in suboptimal precision and recall. To address these problems, we propose UPicker, a semi-supervised transformer-based particle-picking method with a two-stage training process: unsupervised pretraining and supervised fine-tuning. During the unsupervised pretraining, an Adaptive Laplacian of Gaussian region proposal generator is proposed to obtain pseudo-labels from unlabeled data for initial feature learning. For the supervised fine-tuning, UPicker only needs a small amount of labeled data to achieve high accuracy in particle picking. To further enhance model performance, UPicker employs a contrastive denoising training strategy to reduce redundant detections and accelerate convergence, along with a hybrid data augmentation strategy to deal with limited labeled data. Comprehensive experiments on both simulated and experimental datasets demonstrate that UPicker outperforms state-of-the-art particle-picking methods in terms of accuracy and robustness while requiring fewer labeled data than other transformer-based models. Furthermore, ablation studies demonstrate the effectiveness and necessity of each component of UPicker. The source code and data are available at https:\/\/github.com\/JachyLikeCoding\/UPicker.<\/jats:p>","DOI":"10.1093\/bib\/bbae636","type":"journal-article","created":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T01:57:21Z","timestamp":1733882241000},"source":"Crossref","is-referenced-by-count":2,"title":["UPicker: a semi-supervised particle picking transformer method for cryo-EM micrographs"],"prefix":"10.1093","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1900-1284","authenticated-orcid":false,"given":"Chi","family":"Zhang","sequence":"first","affiliation":[{"name":"State Key Laboratory of CAD&CG, Zhejiang University , Hangzhou 310058, Zhejiang ,","place":["China"]}]},{"given":"Yiran","family":"Cheng","sequence":"additional","affiliation":[{"name":"Research Center for Mathematics and Interdisciplinary Sciences, Shandong University , Qingdao 266000, Shandong ,","place":["China"]}]},{"given":"Kaiwen","family":"Feng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of CAD&CG, Zhejiang University , Hangzhou 310058, Zhejiang ,","place":["China"]}]},{"given":"Fa","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Medical Technology, Beijing Institude of Technology , Beijing 100081 ,","place":["China"]}]},{"given":"Renmin","family":"Han","sequence":"additional","affiliation":[{"name":"Research Center for Mathematics and Interdisciplinary Sciences, Shandong University , Qingdao 266000, Shandong ,","place":["China"]}]},{"given":"Jieqing","family":"Feng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of CAD&CG, Zhejiang University , Hangzhou 310058, Zhejiang 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