{"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/fully-bayesian-vib-deepssm","title":"Fully Bayesian VIB-DeepSSM","arxiv_id":"2305.05797","date":"2023-05-09","proceeding":null,"authors":["Jadie Adams","Shireen Elhabian"],"abstract":"Statistical shape modeling (SSM) enables population-based quantitative analysis of anatomical shapes, informing clinical diagnosis. Deep learning approaches predict correspondence-based SSM directly from unsegmented 3D images but require calibrated uncertainty quantification, motivating Bayesian formulations. Variational information bottleneck DeepSSM (VIB-DeepSSM) is an effective, principled framework for predicting probabilistic shapes of anatomy from images with aleatoric uncertainty quantification. However, VIB is only half-Bayesian and lacks epistemic uncertainty inference. We derive a fully Bayesian VIB formulation and demonstrate the efficacy of two scalable implementation approaches: concrete dropout and batch ensemble. Additionally, we introduce a novel combination of the two that further enhances uncertainty calibration via multimodal marginalization. Experiments on synthetic shapes and left atrium data demonstrate that the fully Bayesian VIB network predicts SSM from images with improved uncertainty reasoning without sacrificing accuracy.","url_abs":"https://arxiv.org/abs/2305.05797v2","url_pdf":"https://arxiv.org/pdf/2305.05797v2.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":"fully-bayesian-vib-deepssm","repo_url":"https://github.com/jadie1/bvib-deepssm","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"anatomy","task_name":"Anatomy"},{"task_slug":"uncertainty-quantification","task_name":"Uncertainty Quantification"},{"task_slug":"variational-inference","task_name":"Variational Inference"}],"methods":[{"method_slug":"concrete-dropout","method_name":"Concrete Dropout"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"variational-inference","method_name":"Variational Inference"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2305.05797","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.05797"}},"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/jadie1/bvib-deepssm","reach":null}],"summary":{"ran_fixture":1,"ran_honours":1,"ran_draft_wrong":2,"unverified":1},"by_repo_kind":{"official":{"samples":5,"ran":4,"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":5,"samples":[{"code_sha256_prefix":"8fd8d08e5dbb76c4","entry":"get_pca","repo":"jadie1/bvib-deepssm","repo_kind":"official","path":"SS_loaders.py","file_url":"https://github.com/jadie1/bvib-deepssm/blob/HEAD/SS_loaders.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"8fd8d08e5dbb76c4"}},{"code_sha256_prefix":"1e8865ed7791d658","entry":"get_points","repo":"jadie1/bvib-deepssm","repo_kind":"official","path":"SS_loaders.py","file_url":"https://github.com/jadie1/bvib-deepssm/blob/HEAD/SS_loaders.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"1e8865ed7791d658"}},{"code_sha256_prefix":"0e30ea62e7579bda","entry":"set_scheduler","repo":"jadie1/bvib-deepssm","repo_kind":"official","path":"trainer.py","file_url":"https://github.com/jadie1/bvib-deepssm/blob/HEAD/trainer.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"0e30ea62e7579bda"}},{"code_sha256_prefix":"f339ab5634d8fce6","entry":"weight_init","repo":"jadie1/bvib-deepssm","repo_kind":"official","path":"trainer.py","file_url":"https://github.com/jadie1/bvib-deepssm/blob/HEAD/trainer.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"f339ab5634d8fce6"}},{"code_sha256_prefix":"a7823755ada2a194","entry":"test_mse","repo":"jadie1/bvib-deepssm","repo_kind":"official","path":"trainer.py","file_url":"https://github.com/jadie1/bvib-deepssm/blob/HEAD/trainer.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":"a7823755ada2a194"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}