{"id":"W4295520044","doi":"10.1109/embc48229.2022.9871249","title":"Skin lesion segmentation using a semi-supervised U-NetSC model with an adaptive loss function","year":2022,"lang":"en","type":"article","venue":"2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Segmentation; Jaccard index; Artificial intelligence; Computer science; Pattern recognition (psychology); Ground truth; Scale-space segmentation; Pixel; Image segmentation; Sørensen–Dice coefficient; Feature (linguistics)","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":[0.0008405914,0.0008950504,0.0009512944,0.0007012646,0.0004374182,0.0009289688,0.001992412,0.001739,0.001732929],"category_scores_gemma":[0.00125794,0.0005637194,0.0009755048,0.0004901292,0.0006867292,0.000859129,0.0009007813,0.001159796,0.0005639754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001383646,"about_ca_system_score_gemma":0.001413722,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01258903,"about_ca_topic_score_gemma":0.01477308,"domain_scores_codex":[0.9996095,0.00007257271,0.00002041545,0.0001477745,0.00009288487,0.00005690765],"domain_scores_gemma":[0.999479,0.0001525861,0.00006220197,0.00004783621,0.0002204738,0.00003796771],"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":[0.0002713093,0.0001669577,0.001846271,0.00008030557,0.0001054287,0.0001272919,0.00007472236,0.8296974,0.009040929,0.003492514,0.003594392,0.1515025],"study_design_scores_gemma":[0.000002419273,0.00001585143,0.00008977007,0.000002910417,0.000005451421,0.0000117504,0.000001533113,0.998768,0.0006219721,0.0003394208,0.0001375194,0.000003278812],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05539306,0.0005938591,0.9377902,0.0004281948,0.0001189268,0.0001537473,0.0002169457,0.00186073,0.003444343],"genre_scores_gemma":[0.7945603,0.0003106764,0.1909927,0.0006206924,0.0001000835,0.0003649922,0.0007016246,0.0001751175,0.01217387],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01258903,"threshold_uncertainty_score":0.02503151,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05896084186414619,"score_gpt":0.3030970731034892,"score_spread":0.244136231239343,"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."}}