{"id":"W4407361829","doi":"10.1109/tpami.2025.3540644","title":"Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Artificial intelligence; Computer science; Image (mathematics); Pattern recognition (psychology); Representation (politics); Medical imaging; Contextual image classification; Computer vision; Mathematics; Natural language processing","routes":{"ca_aff":true,"ca_fund":true,"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":[0.006352226,0.001100777,0.001724375,0.001296427,0.0009645302,0.00130228,0.002827273,0.002578049,0.001968806],"category_scores_gemma":[0.02225877,0.0006398034,0.0009968232,0.0009812088,0.003062884,0.002712763,0.002832889,0.002901429,0.0005131566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001315579,"about_ca_system_score_gemma":0.001345546,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002028086,"about_ca_topic_score_gemma":0.001968784,"domain_scores_codex":[0.9963325,0.001345065,0.0002000512,0.001015349,0.0008914038,0.0002157048],"domain_scores_gemma":[0.9909179,0.005743571,0.0007445482,0.001510893,0.0008450626,0.0002380266],"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":[0.0009583016,0.0003363855,0.009942666,0.0003363345,0.0002019308,0.0006446667,0.0003374185,0.3207659,0.01307828,0.04249372,0.007051771,0.6038526],"study_design_scores_gemma":[0.00004086427,0.0001851409,0.001192591,0.00002675235,0.00003822724,0.0004227586,0.00004547821,0.9637753,0.008134285,0.02477967,0.001333607,0.00002533939],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03729239,0.0004306537,0.9597683,0.0005253687,0.00004623358,0.000101172,0.00007670892,0.0006803747,0.001078822],"genre_scores_gemma":[0.705853,0.0003663001,0.2894998,0.0006192115,0.00016702,0.0002784789,0.000598742,0.00018676,0.002430634],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006352226,"threshold_uncertainty_score":0.03359419,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009473576828522392,"score_gpt":0.2801718983699648,"score_spread":0.2706983215414424,"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."}}