{"id":"W325298687","doi":"","title":"Image Analysis in Support of Computer-Assisted Cervical Cancer Screening","year":2013,"lang":"en","type":"article","venue":"KTH Publication Database DiVA (KTH Royal Institute of Technology)","topic":"AI in cancer detection","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cervical cancer; Medicine; Population; Quarter (Canadian coin); Cancer; Disease; Environmental health; Geography; Pathology; Internal medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006037802,0.0004936313,0.0004743286,0.001255812,0.000294601,0.001362798,0.001200968,0.0006870208,0.01033104],"category_scores_gemma":[0.003465867,0.0002413032,0.000380424,0.0008240847,0.0002824848,0.0006614254,0.0007278853,0.000453008,0.004299966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003634879,"about_ca_system_score_gemma":0.0005667493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001416858,"about_ca_topic_score_gemma":0.001398742,"domain_scores_codex":[0.9994696,0.00008334871,0.00003467394,0.00008011601,0.0002905075,0.00004179828],"domain_scores_gemma":[0.9986581,0.0005166406,0.00007037991,0.0001827657,0.0005306387,0.00004159251],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005609976,0.0001240534,0.0009390123,0.0004259879,0.0000581262,0.0003718616,0.0002540647,0.02381911,0.117296,0.005661743,0.02836693,0.8221222],"study_design_scores_gemma":[0.000070814,0.000144406,0.002809348,0.00008159421,0.0000357904,0.0007380001,0.0001295512,0.8150083,0.1100522,0.005494869,0.06537769,0.00005743405],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0124678,0.0003847225,0.9640995,0.0003198203,0.0001287502,0.00020458,0.0006991776,0.01609386,0.005601735],"genre_scores_gemma":[0.1555775,0.0007164667,0.8297781,0.0001850585,0.0001244379,0.0004234727,0.002156277,0.001011012,0.01002768],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01033104,"threshold_uncertainty_score":0.03456074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02223391184587433,"score_gpt":0.2804927783751217,"score_spread":0.2582588665292474,"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."}}