{"id":"W4394692788","doi":"10.1007/s11548-024-03104-3","title":"LensePro: label noise-tolerant prototype-based network for improving cancer detection in prostate ultrasound with limited annotations","year":2024,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"AI in cancer detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Computer science; Ultrasound; Prostate cancer; Noise (video); Cancer detection; Cancer; Medicine; Artificial intelligence; Radiology; Internal medicine","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":[0.001305637,0.001108358,0.001260645,0.001121442,0.0008352447,0.0008178194,0.002968594,0.001642066,0.002453393],"category_scores_gemma":[0.004960115,0.0004659044,0.0005673073,0.0006989927,0.0005372183,0.002111033,0.001973467,0.001282946,0.0009107773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008766719,"about_ca_system_score_gemma":0.0009604311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00541191,"about_ca_topic_score_gemma":0.009422065,"domain_scores_codex":[0.9991442,0.0001552413,0.00003015024,0.0003131678,0.0002437759,0.0001134575],"domain_scores_gemma":[0.9981237,0.0007899618,0.0001437997,0.0003204616,0.0005024035,0.0001196867],"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":[0.002166555,0.0007844561,0.005637886,0.0003697213,0.0002132357,0.0003361734,0.0002255584,0.1302533,0.05372578,0.003285559,0.01976353,0.7832382],"study_design_scores_gemma":[0.00004224127,0.0002205368,0.0009020395,0.00001336259,0.00003912648,0.0001045053,0.00003944487,0.9828503,0.01219194,0.001999699,0.001574985,0.00002168226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1260768,0.00125371,0.8555159,0.0007982622,0.0004498393,0.000295978,0.00121566,0.01148985,0.00290405],"genre_scores_gemma":[0.6795957,0.0004090968,0.3063126,0.0006403948,0.0002157726,0.0003279358,0.003016572,0.0004315566,0.009050389],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00541191,"threshold_uncertainty_score":0.01076084,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01757174294646738,"score_gpt":0.2667502138212859,"score_spread":0.2491784708748186,"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."}}