{"id":"W4403649873","doi":"10.1007/978-3-031-72390-2_50","title":"FACMIC: Federated Adaptative CLIP Model for Medical Image Classification","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"AI in cancer detection","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Image (mathematics); Artificial intelligence; Computer vision; Information retrieval; Computer graphics (images)","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":[0.0005611952,0.0009442951,0.000826312,0.0009422092,0.0003101453,0.0009995045,0.001938482,0.0009486073,0.008158062],"category_scores_gemma":[0.001144192,0.0003095351,0.001187215,0.001063884,0.0002596826,0.001027897,0.001215985,0.001501731,0.003856717],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005844025,"about_ca_system_score_gemma":0.00079235,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006526867,"about_ca_topic_score_gemma":0.008321431,"domain_scores_codex":[0.9996785,0.00003602966,0.00001157079,0.00008213724,0.0001498784,0.00004185768],"domain_scores_gemma":[0.9996811,0.0000719066,0.00001478613,0.0000806044,0.0001265644,0.00002511239],"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":[0.0003628594,0.0001930172,0.0005307728,0.0001101967,0.0001084783,0.00009823151,0.00003455572,0.05290858,0.02172085,0.003039125,0.02916447,0.8917288],"study_design_scores_gemma":[0.00001441313,0.00007084811,0.0004486043,0.00001290224,0.00003344251,0.0001250694,0.00001237721,0.9752921,0.01191362,0.003619432,0.008438312,0.00001893454],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004364108,0.0004026606,0.9835718,0.0001225092,0.0001623639,0.0001093797,0.0008656355,0.008776059,0.001625558],"genre_scores_gemma":[0.154071,0.0009991215,0.8191738,0.0006254191,0.0003482657,0.0004564328,0.006094526,0.001235327,0.01699615],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008158062,"threshold_uncertainty_score":0.02729142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04088328140346871,"score_gpt":0.3010718004089042,"score_spread":0.2601885190054355,"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."}}