{"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/subject2vec-generative-discriminative","title":"Subject2Vec: Generative-Discriminative Approach from a Set of Image Patches to a Vector","arxiv_id":"1806.11217","date":"2018-06-28","proceeding":null,"authors":["Sumedha Singla","Mingming Gong","Siamak Ravanbakhsh","Frank Sciurba","Barnabas Poczos","Kayhan N. Batmanghelich"],"abstract":"We propose an attention-based method that aggregates local image features to\na subject-level representation for predicting disease severity. In contrast to\nclassical deep learning that requires a fixed dimensional input, our method\noperates on a set of image patches; hence it can accommodate variable length\ninput image without image resizing. The model learns a clinically interpretable\nsubject-level representation that is reflective of the disease severity. Our\nmodel consists of three mutually dependent modules which regulate each other:\n(1) a discriminative network that learns a fixed-length representation from\nlocal features and maps them to disease severity; (2) an attention mechanism\nthat provides interpretability by focusing on the areas of the anatomy that\ncontribute the most to the prediction task; and (3) a generative network that\nencourages the diversity of the local latent features. The generative term\nensures that the attention weights are non-degenerate while maintaining the\nrelevance of the local regions to the disease severity. We train our model\nend-to-end in the context of a large-scale lung CT study of Chronic Obstructive\nPulmonary Disease (COPD). Our model gives state-of-the art performance in\npredicting clinical measures of severity for COPD. The distribution of the\nattention provides the regional relevance of lung tissue to the clinical\nmeasurements.","url_abs":"http://arxiv.org/abs/1806.11217v1","url_pdf":"http://arxiv.org/pdf/1806.11217v1.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":"subject2vec-generative-discriminative","repo_url":"https://github.com/batmanlab/subject2vec","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"anatomy","task_name":"Anatomy"}],"methods":[{"method_slug":"interpretability","method_name":"Interpretability"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1806.11217","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.11217"}},"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/batmanlab/subject2vec","reach":null}],"summary":{"ran_violates":1},"by_repo_kind":{"listed":{"samples":1,"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":1,"samples":[{"code_sha256_prefix":"8516cc5177413768","entry":"R2","repo":"batmanlab/subject2vec","repo_kind":"listed","path":"src/COPD_Sub2Vec_Regression.py","file_url":"https://github.com/batmanlab/subject2vec/blob/HEAD/src/COPD_Sub2Vec_Regression.py","link_basis":"first_harvest_node","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"8516cc5177413768"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}