{"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/one-shot-medical-landmark-detection","title":"One-Shot Medical Landmark Detection","arxiv_id":"2103.04527","date":"2021-03-08","proceeding":null,"authors":["Qingsong Yao","Quan Quan","Li Xiao","S. Kevin Zhou"],"abstract":"The success of deep learning methods relies on the availability of a large number of datasets with annotations; however, curating such datasets is burdensome, especially for medical images. To relieve such a burden for a landmark detection task, we explore the feasibility of using only a single annotated image and propose a novel framework named Cascade Comparing to Detect (CC2D) for one-shot landmark detection. CC2D consists of two stages: 1) Self-supervised learning (CC2D-SSL) and 2) Training with pseudo-labels (CC2D-TPL). CC2D-SSL captures the consistent anatomical information in a coarse-to-fine fashion by comparing the cascade feature representations and generates predictions on the training set. CC2D-TPL further improves the performance by training a new landmark detector with those predictions. The effectiveness of CC2D is evaluated on a widely-used public dataset of cephalometric landmark detection, which achieves a competitive detection accuracy of 81.01\\% within 4.0mm, comparable to the state-of-the-art fully-supervised methods using a lot more than one training image.","url_abs":"https://arxiv.org/abs/2103.04527v1","url_pdf":"https://arxiv.org/pdf/2103.04527v1.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":"one-shot-medical-landmark-detection","repo_url":"https://github.com/ICT-MIRACLE-lab/Oneshot_landmark_detection","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"one-shot-medical-landmark-detection","repo_url":"https://github.com/Curli-quan/oneshot-medical-landmark","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"self-supervised-learning","task_name":"Self-Supervised Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2103.04527","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.04527"}},"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/Curli-quan/oneshot-medical-landmark","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/ICT-MIRACLE-lab/Oneshot_landmark_detection","reach":{"status":"unanswered"}}],"summary":{"ran_honours":3},"by_repo_kind":{"listed":{"samples":3,"ran":3,"repositories":1}},"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":0,"samples":[{"code_sha256_prefix":"ce03d63eb106a29a","entry":"L1Loss","repo":"Curli-quan/oneshot-medical-landmark","repo_kind":"listed","path":"scripts/self_train.py","file_url":"https://github.com/Curli-quan/oneshot-medical-landmark/blob/HEAD/scripts/self_train.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"ce03d63eb106a29a"}},{"code_sha256_prefix":"eb19c575ed458088","entry":"focal_loss","repo":"Curli-quan/oneshot-medical-landmark","repo_kind":"listed","path":"scripts/self_train.py","file_url":"https://github.com/Curli-quan/oneshot-medical-landmark/blob/HEAD/scripts/self_train.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"eb19c575ed458088"}},{"code_sha256_prefix":"c6da709fcae5afec","entry":"gray_to_PIL","repo":"Curli-quan/oneshot-medical-landmark","repo_kind":"listed","path":"scripts/self_train.py","file_url":"https://github.com/Curli-quan/oneshot-medical-landmark/blob/HEAD/scripts/self_train.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"c6da709fcae5afec"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}