{"id":"W3178812510","doi":"10.1109/icicsp55539.2022.10050624","title":"Transclaw U-Net: Claw U-Net With Transformers for Medical Image Segmentation","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":97,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Claw; Net (polyhedron); Image segmentation; Artificial intelligence; Computer science; Segmentation; Computer vision; Mathematics; Engineering; Geometry","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":[0.0006539118,0.00105143,0.0005444018,0.001063494,0.0004089429,0.001014686,0.001985985,0.001268898,0.007792443],"category_scores_gemma":[0.001718257,0.0004935671,0.0005992145,0.0007800868,0.000600278,0.001734048,0.00151479,0.0009326786,0.00228501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00089917,"about_ca_system_score_gemma":0.0008909599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005022267,"about_ca_topic_score_gemma":0.008249156,"domain_scores_codex":[0.9997846,0.0000319507,0.00001550198,0.00007314056,0.00005480598,0.00003991953],"domain_scores_gemma":[0.9996716,0.00008697322,0.00003177075,0.00008554859,0.00008749784,0.00003656382],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001166876,0.000290362,0.002465072,0.000307614,0.0001495797,0.0003481518,0.0001011241,0.127843,0.03129512,0.01633832,0.0315825,0.7881123],"study_design_scores_gemma":[0.00004141887,0.0001394117,0.0003378782,0.00001964916,0.00002561595,0.0001772109,0.00001917833,0.9627138,0.02230385,0.008998463,0.005203999,0.00001943681],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04160193,0.0009688521,0.9202836,0.0004614,0.0002693445,0.0002671885,0.000914169,0.0252955,0.009937985],"genre_scores_gemma":[0.4726919,0.0006096464,0.5026156,0.0009596588,0.0001168077,0.0003260993,0.004112964,0.001787044,0.01678034],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007792443,"threshold_uncertainty_score":0.02606833,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01672159803478539,"score_gpt":0.2993248054391024,"score_spread":0.282603207404317,"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."}}