{"id":"W2526499595","doi":"10.1007/978-3-319-46723-8_53","title":"Topology Aware Fully Convolutional Networks for Histology Gland Segmentation","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"AI in cancer detection","field":"Computer Science","cited_by":156,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Segmentation; Artificial intelligence; ENCODE; Convolutional neural network; Prior probability; Dice; Pattern recognition (psychology); Deep learning; Object (grammar); Object detection; Network topology; Image segmentation; Computer vision; Pixel; Topology (electrical circuits); Mathematics; Computer network","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003368325,0.000975745,0.0005775079,0.0008412849,0.000244716,0.0008461811,0.001274995,0.001232187,0.003547115],"category_scores_gemma":[0.0008694726,0.000703635,0.000833747,0.000759346,0.00023497,0.0008577445,0.0008477527,0.00100819,0.002247971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000757998,"about_ca_system_score_gemma":0.000766551,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007009359,"about_ca_topic_score_gemma":0.01481746,"domain_scores_codex":[0.9998555,0.00001798023,0.00000666287,0.00004516778,0.0000469401,0.00002770621],"domain_scores_gemma":[0.9997585,0.00008853187,0.00002267491,0.00005031336,0.00006431937,0.00001561487],"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":[0.000201623,0.00008465075,0.0009821086,0.0002028175,0.0001341554,0.0001726635,0.00005715997,0.2482885,0.0363293,0.00736498,0.01311995,0.693062],"study_design_scores_gemma":[0.000003663983,0.00002137184,0.0006377054,0.00002040455,0.00003022789,0.0001169334,0.000008829076,0.9775401,0.01189547,0.005728703,0.003984975,0.00001148905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02851541,0.003118512,0.9541038,0.0004602915,0.0001445174,0.0000675535,0.00126757,0.006616236,0.005706111],"genre_scores_gemma":[0.4226398,0.004424424,0.5306531,0.0003849983,0.0002239617,0.000143193,0.005599475,0.001270659,0.03466033],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007009359,"threshold_uncertainty_score":0.01393712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01510039814091865,"score_gpt":0.2547135883113253,"score_spread":0.2396131901704067,"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."}}