{"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/end-to-end-localized-deep-learning-for-cryo","title":"End-to-end localized deep learning for Cryo-ET","arxiv_id":"2501.15246","date":"2025-01-25","proceeding":null,"authors":["Vinith Kishore","Valentin Debarnot","Ricardo D. Righetto","AmirEhsan Khorashadizadeh","Benjamin D. Engel","Ivan Dokmanić"],"abstract":"Cryo-electron tomography (cryo-ET) enables 3D visualization of cellular environments. Accurate reconstruction of high-resolution volumes is complicated by the very low signal-to-noise ratio and a restricted range of sample tilts, creating a missing wedge of Fourier information. Recent self-supervised deep learning approaches, which post-process initial reconstructions done by filtered backprojection (FBP), have significantly improved reconstruction quality, but they are computationally expensive, demand large memory, and require retraining for each new dataset. End-to-end supervised learning is an appealing alternative but is impeded by the lack of ground truth and the large memory demands of high-resolution volumetric data. Training on synthetic data often leads to overfitting and poor generalization to real data, and, to date, no general end-to-end deep learning reconstructors exist for cryo-ET. In this work, we introduce CryoLithe, a local, memory-efficient reconstruction network that directly estimates the volume from an aligned tilt-series, overcoming the suboptimal FBP. We demonstrate that leveraging transform-domain locality makes our network robust to distribution shifts, enabling effective supervised training and giving excellent results on real data -- without retraining or fine-tuning.","url_abs":"https://arxiv.org/abs/2501.15246v2","url_pdf":"https://arxiv.org/pdf/2501.15246v2.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":"end-to-end-localized-deep-learning-for-cryo","repo_url":"https://github.com/swing-research/cryolithe","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"electron-tomography","task_name":"Electron Tomography"}],"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}