{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001081428,0.001297295,0.0008546525,0.0011679,0.0004237297,0.001366143,0.002256894,0.001615687,0.002668333],"category_scores_gemma":[0.00350125,0.0007799088,0.001293277,0.00101651,0.0006623549,0.00222651,0.002022502,0.001607953,0.00115051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00112365,"about_ca_system_score_gemma":0.0009542261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009121102,"about_ca_topic_score_gemma":0.01442141,"domain_scores_codex":[0.9995698,0.00009959765,0.00002650299,0.0001285462,0.0001321556,0.0000434027],"domain_scores_gemma":[0.999414,0.0002558465,0.00006177819,0.0001263283,0.0001044293,0.00003755873],"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":[0.0002752906,0.00008787286,0.001744562,0.0002057885,0.0002271545,0.0001655758,0.0001170884,0.5747958,0.0138242,0.01450113,0.01058103,0.3834745],"study_design_scores_gemma":[0.00000821453,0.00001675532,0.00009993545,0.000008647591,0.00001113076,0.00003845949,0.000007761281,0.9913706,0.001834472,0.004598118,0.00199801,0.000007876965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01295137,0.0006911585,0.979763,0.0004504251,0.0001432707,0.00006799419,0.0003849611,0.003897971,0.001649906],"genre_scores_gemma":[0.2698251,0.001089395,0.7175177,0.0008568464,0.0001996538,0.0002722516,0.002478256,0.001461284,0.00629945],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009121102,"threshold_uncertainty_score":0.01813602,"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."}}