{"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/the-lodopab-ct-dataset-a-benchmark-dataset","title":"The LoDoPaB-CT Dataset: A Benchmark Dataset for Low-Dose CT Reconstruction Methods","arxiv_id":"1910.01113","date":"2019-10-01","proceeding":null,"authors":["Johannes Leuschner","Maximilian Schmidt","Daniel Otero Baguer","Peter Maaß"],"abstract":"Deep Learning approaches for solving Inverse Problems in imaging have become very effective and are demonstrated to be quite competitive in the field. Comparing these approaches is a challenging task since they highly rely on the data and the setup that is used for training. We provide a public dataset of computed tomography images and simulated low-dose measurements suitable for training this kind of methods. With the LoDoPaB-CT Dataset we aim to create a benchmark that allows for a fair comparison. It contains over 40,000 scan slices from around 800 patients selected from the LIDC/IDRI Database. In this paper we describe how we processed the original slices and how we simulated the measurements. We also include first baseline results.","url_abs":"https://arxiv.org/abs/1910.01113v2","url_pdf":"https://arxiv.org/pdf/1910.01113v2.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":"the-lodopab-ct-dataset-a-benchmark-dataset","repo_url":"https://github.com/liutianlin0121/ISTA-U-Net","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"ct-reconstruction","task_name":"CT Reconstruction"}],"methods":[],"datasets_introduced":[{"slug":"lodopab-ct","name":"LoDoPaB-CT","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1910.01113","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}