{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/hydrapicker-fully-automated-particle-picking","title":"HydraPicker: Fully Automated Particle Picking in Cryo-EM by Utilizing Dataset Bias in Single Shot Detection","arxiv_id":null,"date":"2019-09-09","proceeding":null,"authors":["Abbas Masoumzadeh","Marcus Brubaker"],"abstract":"Particle picking in cryo-EM is a form of object detection for noisy, low contrast, and out-of-focus microscopy images, taken of different (unknown) structures. This paper presents a fully automated approach which, for the first time, explicitly considers training on multiple structures, while simultaneously learning both specialized models for each structure used for training and a generic model that can be applied to unseen structures. The presented architecture is fully convolutional and divided into two parts: (i) a portion which shares its weights across all structures and (ii) N+1 parallel sets of sub-architectures, N of which are specialized to the structures used for training and a generic model whose weights are tied to the layers for the specialized models. Experiments reveal improvements in multiple use cases over the-state-of-art and present additional possibilities to practitioners.","url_abs":"https://www.researchgate.net/publication/336444905_HydraPicker_Fully_Automated_Particle_Picking_in_Cryo-EM_by_Utilizing_Dataset_Bias_in_Single_Shot_Detection","url_pdf":"https://bmvc2019.org/wp-content/uploads/papers/1044-paper.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"hydrapicker-fully-automated-particle-picking","repo_url":"https://github.com/AbbasMZ/HydraPicker_Colab","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}