{"id":"W3109770947","doi":"10.18280/ria.340506","title":"Lightweight Deep Learning for Malaria Parasite Detection Using Cell-Image of Blood Smear Images","year":2020,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Digital Imaging for Blood Diseases","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Malaria; Blood smear; Transfer of learning; Parasite hosting; Artificial intelligence; Deep learning; Infectious disease (medical specialty); Computer science; Anopheles; Population; Malarial parasites; Pattern recognition (psychology); Disease; Machine learning; Immunology; Biology; Plasmodium falciparum; Medicine; Pathology; Environmental health","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001903483,0.000215599,0.000278796,0.0001161073,0.0001894468,0.0002842047,0.0007297765,0.00005954676,0.00002543331],"category_scores_gemma":[0.0003083661,0.0002314146,0.0002188752,0.000660493,0.0001018595,0.0009417217,0.0002221519,0.0001601906,0.0001014538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001754935,"about_ca_system_score_gemma":0.00004096064,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001759671,"about_ca_topic_score_gemma":0.000001124856,"domain_scores_codex":[0.998248,0.00005694047,0.0005018606,0.0005883703,0.0002105738,0.0003942859],"domain_scores_gemma":[0.9987112,0.0001778604,0.000263009,0.000423262,0.0002303103,0.0001943938],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006751043,0.0006119289,0.001221821,0.0007154953,0.00009172152,0.00005495802,0.002842112,0.07133075,0.8502744,0.003927303,0.00007721196,0.06878482],"study_design_scores_gemma":[0.00005223381,0.0001518876,0.00002737697,0.00002994047,0.00004427806,0.000009277263,0.0001237547,0.4713563,0.5272052,0.0004468512,0.0004020831,0.0001508078],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07371616,0.0005683705,0.9233027,0.0003553921,0.0001628833,0.0003294517,0.000007156921,0.0002112814,0.001346569],"genre_scores_gemma":[0.9472795,0.0000222579,0.05230467,0.00008303548,0.00009029784,0.00001658466,0.000004598498,0.00002925695,0.0001698638],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8735633,"threshold_uncertainty_score":0.9436812,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02882648910005333,"score_gpt":0.2597074889007104,"score_spread":0.2308809998006571,"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."}}