{"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/xlsor-a-robust-and-accurate-lung-segmentor-on","title":"XLSor: A Robust and Accurate Lung Segmentor on Chest X-Rays Using Criss-Cross Attention and Customized Radiorealistic Abnormalities Generation","arxiv_id":"1904.09229","date":"2019-04-19","proceeding":null,"authors":["Youbao Tang","Yu-Xing Tang","Jing Xiao","Ronald M. Summers"],"abstract":"This paper proposes a novel framework for lung segmentation in chest X-rays.\nIt consists of two key contributions, a criss-cross attention based\nsegmentation network and radiorealistic chest X-ray image synthesis (i.e. a\nsynthesized radiograph that appears anatomically realistic) for data\naugmentation. The criss-cross attention modules capture rich global contextual\ninformation in both horizontal and vertical directions for all the pixels thus\nfacilitating accurate lung segmentation. To reduce the manual annotation burden\nand to train a robust lung segmentor that can be adapted to pathological lungs\nwith hazy lung boundaries, an image-to-image translation module is employed to\nsynthesize radiorealistic abnormal CXRs from the source of normal ones for data\naugmentation. The lung masks of synthetic abnormal CXRs are propagated from the\nsegmentation results of their normal counterparts, and then serve as pseudo\nmasks for robust segmentor training. In addition, we annotate 100 CXRs with\nlung masks on a more challenging NIH Chest X-ray dataset containing both\nposterioranterior and anteroposterior views for evaluation. Extensive\nexperiments validate the robustness and effectiveness of the proposed\nframework. The code and data can be found from\nhttps://github.com/rsummers11/CADLab/tree/master/Lung_Segmentation_XLSor .","url_abs":"http://arxiv.org/abs/1904.09229v1","url_pdf":"http://arxiv.org/pdf/1904.09229v1.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":"xlsor-a-robust-and-accurate-lung-segmentor-on","repo_url":"https://github.com/rsummers11/CADLab","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"xlsor-a-robust-and-accurate-lung-segmentor-on","repo_url":"https://github.com/Electro1111/COVID_19_CXR_CLASSIFICATION","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"xlsor-a-robust-and-accurate-lung-segmentor-on","repo_url":"https://github.com/rsummers11/CADLab/tree/master/Lung_Segmentation_XLSor","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"image-to-image-translation","task_name":"Image-to-Image Translation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/lung-nodule-segmentation-on-nih","task":"Lung Nodule Segmentation","dataset":"NIH","model":"U-Net+R+A4","rank_in_archive_order":1,"of":1,"metrics":{"AVD":"0.262","Dice Score":"0.962","Precision":"0.969","Recall":"0.956","VS":"0.985"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1904.09229","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.09229"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/rsummers11/CADLab/tree/master/Lung_Segmentation_XLSor","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/Electro1111/COVID_19_CXR_CLASSIFICATION","reach":{"status":"ok","spdx":"NOASSERTION"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/rsummers11/CADLab","reach":{"status":"ok"}}],"summary":{"ran_draft_wrong":1},"by_repo_kind":{},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":1,"samples":[{"code_sha256_prefix":"583f9780bdd00a45","entry":"conv3x3","repo":null,"repo_kind":null,"path":null,"file_url":null,"link_basis":"identical_code_first_harvested_elsewhere","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"mcp_get_code":{"code_sha256":"583f9780bdd00a45"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}