{"id":"W2952430792","doi":"10.48550/arxiv.1607.05194","title":"HeMIS: Hetero-Modal Image Segmentation","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Modalities; Segmentation; Artificial intelligence; Inference; Embedding; Computer science; Modality (human–computer interaction); Pattern recognition (psychology); Imputation (statistics); Missing data; Image (mathematics); Modal; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006992922,0.0007669061,0.0008062134,0.0008731443,0.0003029566,0.001308288,0.001739363,0.00138928,0.004732057],"category_scores_gemma":[0.001233475,0.0004706476,0.0008931851,0.000822004,0.0007835344,0.00128978,0.001937756,0.001629445,0.002077874],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006308745,"about_ca_system_score_gemma":0.0007568388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00134736,"about_ca_topic_score_gemma":0.002525822,"domain_scores_codex":[0.9995492,0.00006935258,0.00001633086,0.000153651,0.000163401,0.0000480542],"domain_scores_gemma":[0.9997119,0.00006946999,0.00003449749,0.0001118558,0.00004397341,0.0000283364],"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":[0.0003491031,0.0001389344,0.0009521882,0.0002844557,0.0001952998,0.0002185549,0.0001865086,0.134019,0.08064341,0.03445236,0.01902748,0.7295326],"study_design_scores_gemma":[0.00002198792,0.00007893221,0.0006064448,0.00002576138,0.00002562353,0.000354277,0.00003609881,0.9053578,0.04036282,0.03897829,0.01412413,0.00002792236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004314575,0.000217471,0.9889776,0.0001620231,0.00004921897,0.00004083393,0.0002655677,0.003859123,0.002113448],"genre_scores_gemma":[0.1721694,0.0005075276,0.8145367,0.000508101,0.000155315,0.0001727453,0.001795576,0.001057515,0.009097091],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004732057,"threshold_uncertainty_score":0.01583028,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05206219880962581,"score_gpt":0.2153597130857487,"score_spread":0.1632975142761229,"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."}}