{"id":"W4403075145","doi":"10.1007/978-3-031-73471-7_16","title":"Boosting Vision-Language Models for Histopathology Classification: Predict All at Once","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"AI in cancer detection","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure; Centre Hospitalier de l’Université de Montréal","funders":"","keywords":"Boosting (machine learning); Computer science; Artificial intelligence; Natural language processing; Machine learning","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":[0.001698561,0.0009012832,0.001226328,0.0007571335,0.0003437287,0.0008631928,0.001853848,0.001244104,0.002187861],"category_scores_gemma":[0.002565523,0.0005742465,0.001175281,0.0008703583,0.0002932523,0.0009918696,0.0006929923,0.001645425,0.003218861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005858883,"about_ca_system_score_gemma":0.0007973231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003787609,"about_ca_topic_score_gemma":0.005733625,"domain_scores_codex":[0.9995059,0.0001341597,0.0000218778,0.0001084744,0.0001404867,0.00008903923],"domain_scores_gemma":[0.9991164,0.0003341491,0.00005604737,0.0001150893,0.0003201001,0.00005816566],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002318027,0.0003333577,0.003923164,0.0001327778,0.0001683389,0.0000861227,0.00006404218,0.127412,0.01988964,0.004941399,0.02427457,0.8185428],"study_design_scores_gemma":[0.00000764708,0.00006606876,0.0007007211,0.00001475373,0.00005599728,0.00008096944,0.00001065017,0.9843627,0.005489127,0.006916631,0.002282153,0.00001261506],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04674526,0.003732202,0.93784,0.001442779,0.0004873885,0.000104943,0.0007199207,0.003970799,0.004956829],"genre_scores_gemma":[0.6441966,0.001785728,0.334437,0.0009511219,0.0005661265,0.0001602536,0.001909307,0.0004858916,0.01550787],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003787609,"threshold_uncertainty_score":0.008982897,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03131464875024249,"score_gpt":0.2871003280514584,"score_spread":0.2557856793012159,"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."}}