{"id":"W4396566553","doi":"10.1016/j.displa.2024.102735","title":"Enhancing chest X-ray diagnosis with text-to-image generation: A data augmentation case study","year":2024,"lang":"en","type":"article","venue":"Displays","topic":"AI in cancer detection","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; Montreal Heart Institute","funders":"","keywords":"Computer science; Text generation; Image (mathematics); Artificial intelligence; Natural language processing; Medicine; Computer vision; Radiology","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":[],"consensus_categories":[],"category_scores_codex":[0.000343471,0.0001388929,0.0001019157,0.000119382,0.0001999,0.000662172,0.0005102123,0.00002604231,0.00003750642],"category_scores_gemma":[0.00002257295,0.000119768,0.00001623399,0.0007028015,0.00001593908,0.001470456,0.0003139974,0.0001111402,0.0001404693],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001176057,"about_ca_system_score_gemma":0.0000851017,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004090401,"about_ca_topic_score_gemma":0.002994318,"domain_scores_codex":[0.9985452,0.00005999739,0.0001879641,0.0007262639,0.0003014221,0.0001791415],"domain_scores_gemma":[0.9988021,0.00008910308,0.00003646518,0.0009412281,0.00004819661,0.0000829003],"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":[0.00009117314,0.002005381,0.0173286,0.000737261,0.0008480488,0.03899706,0.1191243,0.01039038,0.08293069,0.003420842,0.06565744,0.6584688],"study_design_scores_gemma":[0.00128955,0.002106669,0.006366964,0.0003253462,0.0002583873,0.002509981,0.007940837,0.9187237,0.0481063,0.00006592578,0.0110626,0.001243723],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3000438,0.00009596013,0.6979698,0.0004893626,0.0006206382,0.0004437501,0.0000162615,0.0001864553,0.0001340499],"genre_scores_gemma":[0.9682701,0.000007110129,0.03079507,0.000142929,0.0003558846,0.0003116468,0.00002196097,0.0000183379,0.00007698409],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9083334,"threshold_uncertainty_score":0.6385339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04809545205826209,"score_gpt":0.3229706963746805,"score_spread":0.2748752443164184,"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."}}