{"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/unsupervised-detection-of-lesions-in-brain","title":"Unsupervised Detection of Lesions in Brain MRI using constrained adversarial auto-encoders","arxiv_id":"1806.04972","date":"2018-06-13","proceeding":null,"authors":["Xiaoran Chen","Ender Konukoglu"],"abstract":"Lesion detection in brain Magnetic Resonance Images (MRI) remains a\nchallenging task. State-of-the-art approaches are mostly based on supervised\nlearning making use of large annotated datasets. Human beings, on the other\nhand, even non-experts, can detect most abnormal lesions after seeing a handful\nof healthy brain images. Replicating this capability of using prior information\non the appearance of healthy brain structure to detect lesions can help\ncomputers achieve human level abnormality detection, specifically reducing the\nneed for numerous labeled examples and bettering generalization of previously\nunseen lesions. To this end, we study detection of lesion regions in an\nunsupervised manner by learning data distribution of brain MRI of healthy\nsubjects using auto-encoder based methods. We hypothesize that one of the main\nlimitations of the current models is the lack of consistency in latent\nrepresentation. We propose a simple yet effective constraint that helps mapping\nof an image bearing lesion close to its corresponding healthy image in the\nlatent space. We use the Human Connectome Project dataset to learn distribution\nof healthy-appearing brain MRI and report improved detection, in terms of AUC,\nof the lesions in the BRATS challenge dataset.","url_abs":"http://arxiv.org/abs/1806.04972v1","url_pdf":"http://arxiv.org/pdf/1806.04972v1.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":"unsupervised-detection-of-lesions-in-brain","repo_url":"https://github.com/aubreychen9012/cAAE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"lesion-detection","task_name":"Lesion Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1806.04972","atlas_url":"https://app.syntology.ai/?focus=1806.04972","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.04972"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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/aubreychen9012/cAAE","reach":null}],"summary":{"unverified":1},"by_repo_kind":{"listed":{"samples":1,"ran":0,"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":"a13980ce366435d0","entry":"discriminator","repo":"aubreychen9012/cAAE","repo_kind":"listed","path":"model.py","file_url":"https://github.com/aubreychen9012/cAAE/blob/HEAD/model.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"a13980ce366435d0"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}