{"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/whole-milc-generalizing-learned-dynamics","title":"Whole MILC: generalizing learned dynamics across tasks, datasets, and populations","arxiv_id":"2007.16041","date":"2020-07-29","proceeding":null,"authors":["Usman Mahmood","Md Mahfuzur Rahman","Alex Fedorov","Noah Lewis","Zening Fu","Vince D. Calhoun","Sergey M. Plis"],"abstract":"Behavioral changes are the earliest signs of a mental disorder, but arguably, the dynamics of brain function gets affected even earlier. Subsequently, spatio-temporal structure of disorder-specific dynamics is crucial for early diagnosis and understanding the disorder mechanism. A common way of learning discriminatory features relies on training a classifier and evaluating feature importance. Classical classifiers, based on handcrafted features are quite powerful, but suffer the curse of dimensionality when applied to large input dimensions of spatio-temporal data. Deep learning algorithms could handle the problem and a model introspection could highlight discriminatory spatio-temporal regions but need way more samples to train. In this paper we present a novel self supervised training schema which reinforces whole sequence mutual information local to context (whole MILC). We pre-train the whole MILC model on unlabeled and unrelated healthy control data. We test our model on three different disorders (i) Schizophrenia (ii) Autism and (iii) Alzheimers and four different studies. Our algorithm outperforms existing self-supervised pre-training methods and provides competitive classification results to classical machine learning algorithms. Importantly, whole MILC enables attribution of subject diagnosis to specific spatio-temporal regions in the fMRI signal.","url_abs":"https://arxiv.org/abs/2007.16041v2","url_pdf":"https://arxiv.org/pdf/2007.16041v2.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":"whole-milc-generalizing-learned-dynamics","repo_url":"https://github.com/UsmanMahmood27/MILC","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"feature-importance","task_name":"Feature Importance"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2007.16041","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.16041"}},"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/UsmanMahmood27/MILC","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran_honours":1,"ran_draft_wrong":1,"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":0,"samples":[{"code_sha256_prefix":"33a582f5c65e69d0","entry":"calculate_accuracy","repo":"UsmanMahmood27/MILC","repo_kind":"official","path":"src/utils.py","file_url":"https://github.com/UsmanMahmood27/MILC/blob/HEAD/src/utils.py","link_basis":"harvester_set","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"33a582f5c65e69d0"}},{"code_sha256_prefix":"4d6893e38a989635","entry":"save_grad","repo":"UsmanMahmood27/MILC","repo_kind":"official","path":"src/CNN_LSTM_Att_RealData.py","file_url":"https://github.com/UsmanMahmood27/MILC/blob/HEAD/src/CNN_LSTM_Att_RealData.py","link_basis":"plan_row","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"4d6893e38a989635"}},{"code_sha256_prefix":"bc032d964de1263e","entry":"calculate_FP_Max","repo":"UsmanMahmood27/MILC","repo_kind":"official","path":"src/utils.py","file_url":"https://github.com/UsmanMahmood27/MILC/blob/HEAD/src/utils.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":"bc032d964de1263e"}},{"code_sha256_prefix":"63529a1db624ddfd","entry":"calculate_accuracy_by_labels","repo":"UsmanMahmood27/MILC","repo_kind":"official","path":"src/utils.py","file_url":"https://github.com/UsmanMahmood27/MILC/blob/HEAD/src/utils.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":"63529a1db624ddfd"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}