{"id":"W3107625569","doi":"10.1016/j.media.2020.101912","title":"Learning to segment images with classification labels","year":2020,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"AI in cancer detection","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"Sunnybrook Health Science Centre; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Cancer Society","keywords":"Computer science; Segmentation; Artificial intelligence; Ground truth; Annotation; Class (philosophy); Task (project management); Pattern recognition (psychology); Image segmentation; Labeled data; Machine learning","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":[0.001024126,0.001494383,0.001116645,0.002133434,0.000687192,0.001708861,0.001621044,0.002340658,0.003727369],"category_scores_gemma":[0.004182351,0.0004898468,0.001157671,0.001647379,0.0009711416,0.001799511,0.001136104,0.002326959,0.002563162],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001037377,"about_ca_system_score_gemma":0.001581808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006250558,"about_ca_topic_score_gemma":0.008368008,"domain_scores_codex":[0.9991486,0.0001352013,0.00005602042,0.0003857289,0.0001668647,0.0001076449],"domain_scores_gemma":[0.9976956,0.001125779,0.000206849,0.0003663275,0.0005144193,0.00009104154],"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":[0.0004941062,0.0005165667,0.006659168,0.0002941186,0.0001496794,0.0001085116,0.0001403141,0.02594841,0.02849288,0.004867456,0.01596324,0.9163656],"study_design_scores_gemma":[0.00006284886,0.0003117292,0.002512319,0.00008787832,0.0001307943,0.0001868394,0.0001618031,0.9450889,0.02230768,0.02330563,0.005820166,0.00002338741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08180677,0.001319027,0.9002119,0.002314818,0.0003690635,0.000450281,0.001371167,0.007020484,0.005136416],"genre_scores_gemma":[0.5595965,0.001046909,0.4220098,0.00134955,0.0005919905,0.0005718737,0.004463221,0.0003353335,0.01003488],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006250558,"threshold_uncertainty_score":0.01246929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01534197667013368,"score_gpt":0.2692718852778553,"score_spread":0.2539299086077216,"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."}}