{"id":"W4414486781","doi":"10.1007/978-3-032-05559-0_10","title":"Breast Cancer Detection from Multi-view Screening Mammograms with Visual Prompt Tuning","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"AI in cancer detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Sunnybrook Health Science Centre","funders":"","keywords":"Robustness (evolution); Breast cancer; Mammography; Scalability; Feature (linguistics); Medical imaging; Data set; Pattern recognition (psychology)","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":[0.0003797001,0.0004731863,0.000542078,0.0007568139,0.0001436331,0.0006146492,0.0005043148,0.000669862,0.006220034],"category_scores_gemma":[0.001758946,0.0003242476,0.0003812999,0.0005166341,0.0001004261,0.0004351966,0.000816164,0.0003186794,0.001696126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002083502,"about_ca_system_score_gemma":0.0002672088,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001316119,"about_ca_topic_score_gemma":0.00250933,"domain_scores_codex":[0.9998067,0.00002828849,0.00001182517,0.00005054587,0.00007400392,0.00002871621],"domain_scores_gemma":[0.9995512,0.000214257,0.00002877526,0.00006560319,0.0001085031,0.00003167212],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006643754,0.00009321416,0.001168298,0.0001653194,0.00004615523,0.0001109384,0.00003219359,0.01104169,0.1790687,0.0003799025,0.0043477,0.8028816],"study_design_scores_gemma":[0.0001312211,0.000487302,0.0231248,0.00008595283,0.000156214,0.001638593,0.0001196289,0.8223004,0.1360159,0.003751903,0.01211753,0.0000705335],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06374296,0.001957893,0.9150599,0.0002583126,0.0001769508,0.0001406165,0.0008872367,0.01120232,0.006573825],"genre_scores_gemma":[0.4547753,0.0009638904,0.5342535,0.0003032964,0.0001122775,0.0001254198,0.001149247,0.0007723551,0.007544783],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006220034,"threshold_uncertainty_score":0.0208081,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01574486416068844,"score_gpt":0.2615335562261875,"score_spread":0.2457886920654991,"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."}}