{"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/unlocking-the-power-of-spatial-and-temporal","title":"Unlocking the Power of Spatial and Temporal Information in Medical Multimodal Pre-training","arxiv_id":"2405.19654","date":"2024-05-30","proceeding":null,"authors":["Jinxia Yang","Bing Su","Wayne Xin Zhao","Ji-Rong Wen"],"abstract":"Medical vision-language pre-training methods mainly leverage the correspondence between paired medical images and radiological reports. Although multi-view spatial images and temporal sequences of image-report pairs are available in off-the-shelf multi-modal medical datasets, most existing methods have not thoroughly tapped into such extensive supervision signals. In this paper, we introduce the Med-ST framework for fine-grained spatial and temporal modeling to exploit information from multiple spatial views of chest radiographs and temporal historical records. For spatial modeling, Med-ST employs the Mixture of View Expert (MoVE) architecture to integrate different visual features from both frontal and lateral views. To achieve a more comprehensive alignment, Med-ST not only establishes the global alignment between whole images and texts but also introduces modality-weighted local alignment between text tokens and spatial regions of images. For temporal modeling, we propose a novel cross-modal bidirectional cycle consistency objective by forward mapping classification (FMC) and reverse mapping regression (RMR). By perceiving temporal information from simple to complex, Med-ST can learn temporal semantics. Experimental results across four distinct tasks demonstrate the effectiveness of Med-ST, especially in temporal classification tasks. Our code and model are available at https://github.com/SVT-Yang/MedST.","url_abs":"https://arxiv.org/abs/2405.19654v1","url_pdf":"https://arxiv.org/pdf/2405.19654v1.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":"unlocking-the-power-of-spatial-and-temporal","repo_url":"https://github.com/svt-yang/medst","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"temporal-sequences","task_name":"Temporal Sequences"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2405.19654","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.19654"}},"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/svt-yang/medst","reach":{"status":"ok"}}],"summary":{"ran":3,"unverified":6},"by_repo_kind":{"official":{"samples":8,"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":9,"samples":[{"code_sha256_prefix":"e216ceb4e964b6ba","entry":"get_and_proc","repo":"svt-yang/medst","repo_kind":"official","path":"medst/datasets/image_process.py","file_url":"https://github.com/svt-yang/medst/blob/HEAD/medst/datasets/image_process.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"e216ceb4e964b6ba"}},{"code_sha256_prefix":"acbe0cee9e7a1016","entry":"get_imgs","repo":"svt-yang/medst","repo_kind":"official","path":"medst/datasets/utils.py","file_url":"https://github.com/svt-yang/medst/blob/HEAD/medst/datasets/utils.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"acbe0cee9e7a1016"}},{"code_sha256_prefix":"20dbe59400d78b3f","entry":"multimodal_collate_fn","repo":"svt-yang/medst","repo_kind":"official","path":"medst/datasets/pretrain_dataset.py","file_url":"https://github.com/svt-yang/medst/blob/HEAD/medst/datasets/pretrain_dataset.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"20dbe59400d78b3f"}},{"code_sha256_prefix":"888f1a7752457cce","entry":"concat_all_gather","repo":null,"repo_kind":null,"path":null,"file_url":null,"link_basis":"identical_code_first_harvested_elsewhere","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"mcp_get_code":{"code_sha256":"888f1a7752457cce"}},{"code_sha256_prefix":"4203512ae99275fa","entry":"get_MIMIC_img","repo":"svt-yang/medst","repo_kind":"official","path":"medst/datasets/image_process.py","file_url":"https://github.com/svt-yang/medst/blob/HEAD/medst/datasets/image_process.py","link_basis":"plan_row","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"4203512ae99275fa"}},{"code_sha256_prefix":"5aed092fd00aff3b","entry":"resize_img","repo":"svt-yang/medst","repo_kind":"official","path":"medst/datasets/utils.py","file_url":"https://github.com/svt-yang/medst/blob/HEAD/medst/datasets/utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"5aed092fd00aff3b"}},{"code_sha256_prefix":"4fe94741be065e60","entry":"resnet_18","repo":"svt-yang/medst","repo_kind":"official","path":"medst/models/backbones/cnn_backbones.py","file_url":"https://github.com/svt-yang/medst/blob/HEAD/medst/models/backbones/cnn_backbones.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"4fe94741be065e60"}},{"code_sha256_prefix":"47159040b1322a4f","entry":"resnet_34","repo":"svt-yang/medst","repo_kind":"official","path":"medst/models/backbones/cnn_backbones.py","file_url":"https://github.com/svt-yang/medst/blob/HEAD/medst/models/backbones/cnn_backbones.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"47159040b1322a4f"}},{"code_sha256_prefix":"21a1aca3696ae226","entry":"resnet_50","repo":"svt-yang/medst","repo_kind":"official","path":"medst/models/backbones/cnn_backbones.py","file_url":"https://github.com/svt-yang/medst/blob/HEAD/medst/models/backbones/cnn_backbones.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"21a1aca3696ae226"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}