{"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/knowledge-based-in-silico-models-and-dataset-1","title":"Knowledge-based in silico models and dataset for the comparative evaluation of mammography AI for a range of breast characteristics, lesion conspicuities and doses","arxiv_id":"2310.18494","date":"2023-10-27","proceeding":"NeurIPS 2023 11","authors":["Elena Sizikova","Niloufar Saharkhiz","Diksha Sharma","Miguel Lago","Berkman Sahiner","Jana G. Delfino","Aldo Badano"],"abstract":"To generate evidence regarding the safety and efficacy of artificial intelligence (AI) enabled medical devices, AI models need to be evaluated on a diverse population of patient cases, some of which may not be readily available. We propose an evaluation approach for testing medical imaging AI models that relies on in silico imaging pipelines in which stochastic digital models of human anatomy (in object space) with and without pathology are imaged using a digital replica imaging acquisition system to generate realistic synthetic image datasets. Here, we release M-SYNTH, a dataset of cohorts with four breast fibroglandular density distributions imaged at different exposure levels using Monte Carlo x-ray simulations with the publicly available Virtual Imaging Clinical Trial for Regulatory Evaluation (VICTRE) toolkit. We utilize the synthetic dataset to analyze AI model performance and find that model performance decreases with increasing breast density and increases with higher mass density, as expected. As exposure levels decrease, AI model performance drops with the highest performance achieved at exposure levels lower than the nominal recommended dose for the breast type.","url_abs":"https://arxiv.org/abs/2310.18494v1","url_pdf":"https://arxiv.org/pdf/2310.18494v1.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":"knowledge-based-in-silico-models-and-dataset-1","repo_url":"https://github.com/didsr/msynth-release","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"CC0-1.0"}}],"tasks":[{"task_slug":"anatomy","task_name":"Anatomy"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2310.18494","atlas_url":"https://app.syntology.ai/?focus=2310.18494","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.18494"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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/didsr/msynth-release","reach":{"status":"ok","spdx":"CC0-1.0"}}],"summary":{"ran":3,"unverified":1},"by_repo_kind":{"official":{"samples":4,"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":0,"samples":[{"code_sha256_prefix":"14d1b9eaaf58c3b1","entry":"get_lesion_label","repo":"didsr/msynth-release","repo_kind":"official","path":"code/util/util_preprocessing.py","file_url":"https://github.com/didsr/msynth-release/blob/HEAD/code/util/util_preprocessing.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"CC0-1.0","inline_ok":true,"mcp_get_code":{"code_sha256":"14d1b9eaaf58c3b1"}},{"code_sha256_prefix":"00f4bd3ecfecb1f4","entry":"get_model_nickname","repo":"didsr/msynth-release","repo_kind":"official","path":"code/util/util_preprocessing.py","file_url":"https://github.com/didsr/msynth-release/blob/HEAD/code/util/util_preprocessing.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"CC0-1.0","inline_ok":true,"mcp_get_code":{"code_sha256":"00f4bd3ecfecb1f4"}},{"code_sha256_prefix":"80ead9e4dcf417ed","entry":"read_mhd","repo":"didsr/msynth-release","repo_kind":"official","path":"code/util/util.py","file_url":"https://github.com/didsr/msynth-release/blob/HEAD/code/util/util.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"CC0-1.0","inline_ok":true,"mcp_get_code":{"code_sha256":"80ead9e4dcf417ed"}},{"code_sha256_prefix":"9494284b659a4cc6","entry":"get_ba","repo":"didsr/msynth-release","repo_kind":"official","path":"code/util/util_testing.py","file_url":"https://github.com/didsr/msynth-release/blob/HEAD/code/util/util_testing.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"CC0-1.0","inline_ok":true,"mcp_get_code":{"code_sha256":"9494284b659a4cc6"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}