{"id":"W4405739105","doi":"10.1364/laop.2024.w4a.16","title":"Characterizing Breast Tissue Samples with Integrated Imaging","year":2024,"lang":"en","type":"article","venue":"","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; Toronto Metropolitan University; St. Michael's Hospital","funders":"","keywords":"Breast tissue; Computer science; Biomedical engineering; Medicine; Breast cancer; Internal medicine; Cancer","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000799826,0.0006850878,0.0005013439,0.00185805,0.0003358406,0.0006951708,0.0005983508,0.0007708061,0.001383562],"category_scores_gemma":[0.0009175189,0.000499757,0.0003040513,0.001142057,0.0003542668,0.0005398081,0.0004709561,0.0006642152,0.0007075088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002799271,"about_ca_system_score_gemma":0.0003306516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008232555,"about_ca_topic_score_gemma":0.002392149,"domain_scores_codex":[0.9993972,0.00006955006,0.00003601433,0.0002017563,0.0002281255,0.0000674299],"domain_scores_gemma":[0.999544,0.0001079294,0.00005593063,0.0000762766,0.0001890998,0.00002686718],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007464118,0.00002717023,0.001015918,0.00007382395,0.00001614466,0.0000493678,0.00006203831,0.0001642975,0.9845049,0.0001439018,0.00007467707,0.01379304],"study_design_scores_gemma":[0.00001456451,0.0002506449,0.009530175,0.00001646093,0.0001256074,0.0006832097,0.00008804322,0.01126432,0.9749477,0.0002583241,0.002798845,0.00002220513],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4351638,0.003386844,0.5527641,0.000165426,0.00009587665,0.0003612318,0.0007280362,0.002780188,0.00455454],"genre_scores_gemma":[0.4267043,0.00147541,0.5666255,0.0001950325,0.00004894035,0.0004208314,0.0008783359,0.0003452507,0.003306352],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00185805,"threshold_uncertainty_score":0.004628539,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008350827297156423,"score_gpt":0.2788182252639099,"score_spread":0.2704673979667535,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}