{"id":"W4403048090","doi":"10.1007/978-3-031-73471-7_7","title":"Automating MedSAM by Learning Prompts with Weak Few-Shot Supervision","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Computer science; Shot (pellet); Human–computer interaction; Artificial intelligence; Computer graphics (images); Programming language; Natural language processing; Engineering drawing; Engineering","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":[0.001970166,0.002054842,0.001679319,0.001149389,0.0009112422,0.001512833,0.002473566,0.001927999,0.01059074],"category_scores_gemma":[0.009192747,0.0008508279,0.0009999402,0.0008726232,0.0007458726,0.003310072,0.002672158,0.003861378,0.0072403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008481854,"about_ca_system_score_gemma":0.002346424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004567789,"about_ca_topic_score_gemma":0.009480607,"domain_scores_codex":[0.9985292,0.0003871049,0.00007504973,0.0006547367,0.0002332579,0.0001206435],"domain_scores_gemma":[0.9957553,0.002634805,0.0002081131,0.0005391314,0.00064926,0.0002133892],"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":[0.0009533363,0.0003604582,0.001386868,0.0004318528,0.00007841623,0.0001944867,0.0001470753,0.03484586,0.01376449,0.003381173,0.03264895,0.9118071],"study_design_scores_gemma":[0.00007480977,0.0001979334,0.0006151733,0.00007113253,0.00005620353,0.0001943761,0.0001104705,0.9363064,0.02739632,0.0239658,0.01096547,0.00004581244],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01553468,0.0009432794,0.9111988,0.0006033783,0.0005372897,0.0002168225,0.001288521,0.066014,0.003663235],"genre_scores_gemma":[0.3349899,0.0004357174,0.6450628,0.001105061,0.0003058757,0.0002659104,0.003821801,0.002296595,0.01171637],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01059074,"threshold_uncertainty_score":0.03542954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01576934414861179,"score_gpt":0.2590442351092754,"score_spread":0.2432748909606636,"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."}}