{"id":"W4405475062","doi":"10.1561/116.20240048","title":"When Federated Learning Meets Medical Image Analysis: A Systematic Review with Challenges and Solutions","year":2024,"lang":"en","type":"review","venue":"APSIPA Transactions on Signal and Information Processing","topic":"AI in cancer detection","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Image (mathematics); Data science; Information retrieval; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009516231,0.0003998727,0.001357209,0.0007814357,0.0006248437,0.0008073843,0.0002599975,0.0002186257,0.00002776161],"category_scores_gemma":[0.00003231546,0.0002673955,0.0001981981,0.001247413,0.00008671609,0.003713654,0.00002378328,0.0007802208,0.00004160748],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001115172,"about_ca_system_score_gemma":0.0003934003,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006644958,"about_ca_topic_score_gemma":0.00001562996,"domain_scores_codex":[0.9974557,0.0002352943,0.0009842372,0.0003956248,0.0006744171,0.0002547682],"domain_scores_gemma":[0.9987696,0.0001325467,0.0005234812,0.0002039541,0.0002046578,0.0001657952],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"systematic_review","study_design_scores_codex":[0.000001156096,0.000007847102,2.078863e-9,0.4110192,0.0003786597,0.000002120535,0.0004734011,0.00001134383,1.407085e-8,0.0001439575,0.000009728998,0.5879526],"study_design_scores_gemma":[0.000210104,0.0002479114,2.31132e-7,0.8098323,0.01736216,0.0007167499,0.0003302707,0.09308586,0.000001177246,0.0001130883,0.07733754,0.0007626388],"study_design_candidate":"systematic_review","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[2.458781e-8,0.5751549,0.423464,0.0004683261,0.00002975209,0.0004371369,0.000003169193,0.0001731551,0.0002695327],"genre_scores_gemma":[0.0002628626,0.9975034,0.001423817,0.0002109858,0.00001674776,0.0004879533,0.00001997353,0.0000167496,0.00005748878],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.5871899,"threshold_uncertainty_score":0.9999778,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02938859579786442,"score_gpt":0.2824501714748403,"score_spread":0.2530615756769759,"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."}}