{"id":"W4225368580","doi":"10.24963/ijcai.2022/122","title":"MNet: Rethinking 2D/3D Networks for Anisotropic Medical Image Segmentation","year":2022,"lang":"en","type":"article","venue":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"National Key Research and Development Program of China; Southeast University","keywords":"Computer science; Overfitting; Convolutional neural network; Representation (politics); Artificial intelligence; Pattern recognition (psychology); Segmentation; Code (set theory); Convolution (computer science); Noise (video); Embedding; Image (mathematics); Artificial neural 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007748161,0.0002392139,0.0002421487,0.0001611721,0.0007890835,0.0002272013,0.003138941,0.00007879839,0.000282473],"category_scores_gemma":[0.0004287926,0.0002090412,0.000173756,0.0005852371,0.0002174,0.0005233305,0.001203878,0.0006022161,0.00001758442],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002438001,"about_ca_system_score_gemma":0.000109962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002102228,"about_ca_topic_score_gemma":0.00001983986,"domain_scores_codex":[0.996898,0.00003075539,0.0007808053,0.0006424093,0.001289403,0.0003586371],"domain_scores_gemma":[0.9979214,0.0002665384,0.000694094,0.0003049549,0.0007054167,0.0001076118],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000784144,0.0001732352,0.00006757413,0.00001711049,0.00003063566,0.000001054841,0.0009158429,0.008095788,0.007034015,0.951304,0.0006193277,0.03166305],"study_design_scores_gemma":[0.00007845068,0.0001810478,0.00008185965,0.00009130237,0.00001079119,0.00002144917,0.0003719465,0.7018959,0.03612628,0.2599134,0.001003706,0.0002238941],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0117064,0.00002433869,0.9554999,0.02664086,0.001753775,0.00104139,0.00002388376,0.0001859731,0.003123419],"genre_scores_gemma":[0.9585655,0.00006865746,0.03926365,0.001154011,0.0002705952,0.0004704996,0.00001027496,0.00002317705,0.0001735984],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9468591,"threshold_uncertainty_score":0.8524451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06027301354964597,"score_gpt":0.3071543382944801,"score_spread":0.2468813247448341,"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."}}