{"id":"W2750605898","doi":"10.1007/978-3-319-67534-3_17","title":"Uncertainty Driven Multi-loss Fully Convolutional Networks for Histopathology","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"AI in cancer detection","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Weighting; Computer science; Leverage (statistics); Convolutional neural network; Inference; Term (time); Artificial intelligence; Function (biology); Algorithm; Pattern recognition (psychology); Machine learning","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001277958,0.0005952643,0.0006546192,0.0005549803,0.0008635645,0.0004653479,0.004861777,0.0006350995,0.00001330576],"category_scores_gemma":[0.0002242888,0.0005937719,0.0002410263,0.0001689034,0.001811314,0.0006295825,0.001407656,0.000945749,0.00002197318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001254451,"about_ca_system_score_gemma":0.00102986,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003999479,"about_ca_topic_score_gemma":0.0003953777,"domain_scores_codex":[0.9957069,0.00005746144,0.0005611159,0.002070677,0.0006966155,0.0009072691],"domain_scores_gemma":[0.9958506,0.0007203401,0.0006596204,0.002019539,0.0005578717,0.0001920268],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003870725,0.00003215657,0.000117779,0.00004088683,0.00001487404,0.00009092894,0.0003089791,0.5180584,0.00007258038,0.01963746,0.0001464246,0.4614409],"study_design_scores_gemma":[0.0005882034,0.0002478196,0.0004316645,0.0001744422,0.00001142648,0.0001638518,4.775827e-8,0.9308245,0.0000675872,0.05738268,0.009466279,0.0006415622],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00001826015,0.0005539814,0.9888837,0.0009875952,0.007999025,0.000750677,0.00001910688,0.0002060658,0.0005815841],"genre_scores_gemma":[0.1348983,0.00009421431,0.8587698,0.001840413,0.002416119,0.0001256796,0.00002540545,0.00007798236,0.001752065],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4607993,"threshold_uncertainty_score":0.9996514,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02525921922953262,"score_gpt":0.2695038431022251,"score_spread":0.2442446238726925,"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."}}