{"id":"W3216943664","doi":"10.48550/arxiv.2111.14953","title":"Localized Perturbations For Weakly-Supervised Segmentation of Glioma Brain Tumours","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Convolutional neural network; Segmentation; Artificial intelligence; Computer science; Pattern recognition (psychology); Dice; Visualization; Pipeline (software); Similarity (geometry); Sørensen–Dice coefficient; Image segmentation; Mathematics; Image (mathematics)","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":[0.001050391,0.0009825714,0.0006139571,0.0009306849,0.000323055,0.000843442,0.0009325365,0.0009557707,0.0008212841],"category_scores_gemma":[0.003302493,0.0005417539,0.0005486886,0.0004508735,0.000904917,0.0008769488,0.001387575,0.001051585,0.0006374521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008173142,"about_ca_system_score_gemma":0.0008759513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002054025,"about_ca_topic_score_gemma":0.003832995,"domain_scores_codex":[0.9993486,0.000181366,0.0000331897,0.0001985933,0.0001952178,0.00004296923],"domain_scores_gemma":[0.9988652,0.0003620482,0.0002156721,0.0002540323,0.0002322167,0.0000708381],"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":[0.0007351117,0.0001101742,0.003375173,0.0002634192,0.0001107819,0.00027372,0.0004169155,0.3988093,0.2892185,0.007332936,0.003436292,0.2959178],"study_design_scores_gemma":[0.000006240759,0.00003734527,0.0007606667,0.000008352556,0.000008817237,0.00008456879,0.00001976787,0.9532577,0.04074274,0.004048178,0.001011897,0.00001367696],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07829484,0.0002736823,0.9174119,0.0001877118,0.00003427035,0.0000771008,0.0001337075,0.002617402,0.0009692974],"genre_scores_gemma":[0.5859195,0.0002481909,0.4098001,0.0001568316,0.00005486469,0.0001095212,0.0006562722,0.0006264086,0.002428307],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002054025,"threshold_uncertainty_score":0.005930066,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06276065334857993,"score_gpt":0.2238827814494417,"score_spread":0.1611221281008618,"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."}}