{"id":"W4392943516","doi":"10.1109/icmla58977.2023.00301","title":"SAttisUNet: UNet-like Swin Transformer with Attentive Skip Connections for Enhanced Medical Image Segmentation","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ottawa Hospital; University of Ottawa","funders":"","keywords":"Image segmentation; Computer science; Transformer; Computer vision; Segmentation; Artificial intelligence; Electrical engineering; Engineering; Voltage","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":[0.0001407659,0.0001464391,0.0001403559,0.0001001644,0.0002790708,0.00005870411,0.000392953,0.00005675872,0.00009408769],"category_scores_gemma":[0.00001709743,0.0001193901,0.00005988031,0.001108421,0.00008497886,0.0005320997,0.00002806259,0.0001085128,0.0001864064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004127079,"about_ca_system_score_gemma":0.00006800636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001063794,"about_ca_topic_score_gemma":0.0002097277,"domain_scores_codex":[0.9985809,0.00002602828,0.0002275134,0.0004703031,0.0003431221,0.0003520864],"domain_scores_gemma":[0.9990471,0.0003347918,0.00006097321,0.000304048,0.0001117972,0.0001412962],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001578108,0.0004660855,0.0002871518,0.0001096898,0.0002926567,0.00003320943,0.004122477,0.005324124,0.3583116,0.2350407,0.03853849,0.3573159],"study_design_scores_gemma":[0.008051491,0.001180332,0.006475418,0.0001402087,0.0001047327,0.00009816906,0.001591918,0.3348341,0.5716688,0.03160033,0.04246139,0.001793124],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00950557,0.00000742288,0.9807912,0.00644136,0.0001683384,0.0008764375,0.000009964239,0.0006308898,0.001568792],"genre_scores_gemma":[0.6245278,0.0001105569,0.3647403,0.002400772,0.000203118,0.002517308,0.0001848201,0.00005623976,0.005259063],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.616051,"threshold_uncertainty_score":0.4868589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01536237161293592,"score_gpt":0.2977867229878433,"score_spread":0.2824243513749073,"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."}}