{"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/advancing-radiograph-representation-learning","title":"Advancing Radiograph Representation Learning with Masked Record Modeling","arxiv_id":"2301.13155","date":"2023-01-30","proceeding":null,"authors":["Hong-Yu Zhou","Chenyu Lian","Liansheng Wang","Yizhou Yu"],"abstract":"Modern studies in radiograph representation learning rely on either self-supervision to encode invariant semantics or associated radiology reports to incorporate medical expertise, while the complementarity between them is barely noticed. To explore this, we formulate the self- and report-completion as two complementary objectives and present a unified framework based on masked record modeling (MRM). In practice, MRM reconstructs masked image patches and masked report tokens following a multi-task scheme to learn knowledge-enhanced semantic representations. With MRM pre-training, we obtain pre-trained models that can be well transferred to various radiography tasks. Specifically, we find that MRM offers superior performance in label-efficient fine-tuning. For instance, MRM achieves 88.5% mean AUC on CheXpert using 1% labeled data, outperforming previous R$^2$L methods with 100% labels. On NIH ChestX-ray, MRM outperforms the best performing counterpart by about 3% under small labeling ratios. Besides, MRM surpasses self- and report-supervised pre-training in identifying the pneumonia type and the pneumothorax area, sometimes by large margins.","url_abs":"https://arxiv.org/abs/2301.13155v2","url_pdf":"https://arxiv.org/pdf/2301.13155v2.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":"advancing-radiograph-representation-learning","repo_url":"https://github.com/rl4m/mrm-pytorch","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2301.13155","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.13155"}},"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/rl4m/mrm-pytorch","reach":null}],"summary":{"ran":1,"unverified":7},"by_repo_kind":{"official":{"samples":8,"ran":1,"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":"9829b3690392ef17","entry":"BertConfig","repo":"rl4m/mrm-pytorch","repo_kind":"official","path":"model_mrm.py","file_url":"https://github.com/rl4m/mrm-pytorch/blob/HEAD/model_mrm.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"9829b3690392ef17"}},{"code_sha256_prefix":"10e6fbb7187a0639","entry":"BertEncoder","repo":"rl4m/mrm-pytorch","repo_kind":"official","path":"model_mrm.py","file_url":"https://github.com/rl4m/mrm-pytorch/blob/HEAD/model_mrm.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":"10e6fbb7187a0639"}},{"code_sha256_prefix":"6a0e1c9e1ca24ab5","entry":"MRM","repo":"rl4m/mrm-pytorch","repo_kind":"official","path":"model_mrm.py","file_url":"https://github.com/rl4m/mrm-pytorch/blob/HEAD/model_mrm.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":"6a0e1c9e1ca24ab5"}},{"code_sha256_prefix":"74110e1a2c36ac68","entry":"MyBertMaskedLM","repo":"rl4m/mrm-pytorch","repo_kind":"official","path":"model_mrm.py","file_url":"https://github.com/rl4m/mrm-pytorch/blob/HEAD/model_mrm.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":"74110e1a2c36ac68"}},{"code_sha256_prefix":"63bdcb2eaf37a29d","entry":"MyBertModel","repo":"rl4m/mrm-pytorch","repo_kind":"official","path":"model_mrm.py","file_url":"https://github.com/rl4m/mrm-pytorch/blob/HEAD/model_mrm.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":"63bdcb2eaf37a29d"}},{"code_sha256_prefix":"ddbefd1ad8482d18","entry":"get_1d_sincos_pos_embed_from_grid","repo":"rl4m/mrm-pytorch","repo_kind":"official","path":"model_mrm.py","file_url":"https://github.com/rl4m/mrm-pytorch/blob/HEAD/model_mrm.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":"ddbefd1ad8482d18"}},{"code_sha256_prefix":"dfd354ccf91ca5bd","entry":"get_2d_sincos_pos_embed","repo":"rl4m/mrm-pytorch","repo_kind":"official","path":"model_mrm.py","file_url":"https://github.com/rl4m/mrm-pytorch/blob/HEAD/model_mrm.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":"dfd354ccf91ca5bd"}},{"code_sha256_prefix":"21283b827aa73894","entry":"get_2d_sincos_pos_embed_from_grid","repo":"rl4m/mrm-pytorch","repo_kind":"official","path":"model_mrm.py","file_url":"https://github.com/rl4m/mrm-pytorch/blob/HEAD/model_mrm.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":"21283b827aa73894"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}