{"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/local-spatiotemporal-representation-learning","title":"Local Spatiotemporal Representation Learning for Longitudinally-consistent Neuroimage Analysis","arxiv_id":"2206.04281","date":"2022-06-09","proceeding":null,"authors":["Mengwei Ren","Neel Dey","Martin A. Styner","Kelly Botteron","Guido Gerig"],"abstract":"Recent self-supervised advances in medical computer vision exploit global and local anatomical self-similarity for pretraining prior to downstream tasks such as segmentation. However, current methods assume i.i.d. image acquisition, which is invalid in clinical study designs where follow-up longitudinal scans track subject-specific temporal changes. Further, existing self-supervised methods for medically-relevant image-to-image architectures exploit only spatial or temporal self-similarity and only do so via a loss applied at a single image-scale, with naive multi-scale spatiotemporal extensions collapsing to degenerate solutions. To these ends, this paper makes two contributions: (1) It presents a local and multi-scale spatiotemporal representation learning method for image-to-image architectures trained on longitudinal images. It exploits the spatiotemporal self-similarity of learned multi-scale intra-subject features for pretraining and develops several feature-wise regularizations that avoid collapsed identity representations; (2) During finetuning, it proposes a surprisingly simple self-supervised segmentation consistency regularization to exploit intra-subject correlation. Benchmarked in the one-shot segmentation setting, the proposed framework outperforms both well-tuned randomly-initialized baselines and current self-supervised techniques designed for both i.i.d. and longitudinal datasets. These improvements are demonstrated across both longitudinal neurodegenerative adult MRI and developing infant brain MRI and yield both higher performance and longitudinal consistency.","url_abs":"https://arxiv.org/abs/2206.04281v4","url_pdf":"https://arxiv.org/pdf/2206.04281v4.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":"local-spatiotemporal-representation-learning","repo_url":"https://github.com/mengweiren/longitudinal-representation-learning","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"one-shot-segmentation","task_name":"One-Shot Segmentation"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2206.04281","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.04281"}},"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/mengweiren/longitudinal-representation-learning","reach":null}],"summary":{"ran":2,"unverified":2},"by_repo_kind":{"official":{"samples":4,"ran":2,"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":4,"samples":[{"code_sha256_prefix":"5541ef612b619cfa","entry":"Normalize","repo":"mengweiren/longitudinal-representation-learning","repo_kind":"official","path":"src/models/longrep_model.py","file_url":"https://github.com/mengweiren/longitudinal-representation-learning/blob/HEAD/src/models/longrep_model.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"5541ef612b619cfa"}},{"code_sha256_prefix":"db460b3bb645acc2","entry":"Pairwise_Feature_Similarity","repo":"mengweiren/longitudinal-representation-learning","repo_kind":"official","path":"src/models/longrep_model.py","file_url":"https://github.com/mengweiren/longitudinal-representation-learning/blob/HEAD/src/models/longrep_model.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":"db460b3bb645acc2"}},{"code_sha256_prefix":"e6203f8925aabf36","entry":"BaseModel","repo":"mengweiren/longitudinal-representation-learning","repo_kind":"official","path":"src/models/longrep_model.py","file_url":"https://github.com/mengweiren/longitudinal-representation-learning/blob/HEAD/src/models/longrep_model.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":"e6203f8925aabf36"}},{"code_sha256_prefix":"429ce29ad6b21697","entry":"LongRepModel","repo":"mengweiren/longitudinal-representation-learning","repo_kind":"official","path":"src/models/longrep_model.py","file_url":"https://github.com/mengweiren/longitudinal-representation-learning/blob/HEAD/src/models/longrep_model.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":"429ce29ad6b21697"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}