{"id":"W4407566436","doi":"10.1109/tim.2025.3541664","title":"Novel CNN-Based Approach for Burn Severity Assessment and Fine-Grained Boundary Segmentation in Burn Images","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; Montreal Heart Institute; Université de Montréal; University of Alberta; SKiN Health","funders":"","keywords":"Burn-in; Segmentation; Computer science; Image segmentation; Boundary (topology); Pediatric burn; Artificial intelligence; Computer vision; Engineering; Reliability engineering; Medicine; Mathematics; Surgery","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":[0.0004249373,0.001127127,0.0006939859,0.001256112,0.0002950811,0.0006493448,0.0009579371,0.0007512219,0.001991107],"category_scores_gemma":[0.0008071235,0.0003853313,0.0008199715,0.0006147783,0.0002650473,0.0008403035,0.0006960091,0.0008382277,0.0006772255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009116994,"about_ca_system_score_gemma":0.0007831633,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009553581,"about_ca_topic_score_gemma":0.01378865,"domain_scores_codex":[0.9997686,0.00001933786,0.00001145191,0.00008981741,0.00005376076,0.00005713723],"domain_scores_gemma":[0.9998117,0.00003886768,0.00002895831,0.00002712651,0.00007278266,0.00002048837],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000515605,0.0002639285,0.006722248,0.0002402681,0.0001814264,0.0002884064,0.0001454803,0.1216599,0.08831215,0.002465908,0.007835167,0.7713695],"study_design_scores_gemma":[0.00001228861,0.00008475902,0.002938938,0.00002214965,0.00005206174,0.0001243515,0.00002706156,0.9753464,0.01782211,0.001737059,0.001819977,0.000012846],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1530815,0.002327398,0.8295521,0.0005107117,0.0002250685,0.0003025183,0.001084377,0.006822016,0.006094498],"genre_scores_gemma":[0.695874,0.001120021,0.2901768,0.0005385161,0.0001670625,0.0002264457,0.002922063,0.0003630144,0.008611978],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009553581,"threshold_uncertainty_score":0.01899594,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0259327235583379,"score_gpt":0.2620148287273842,"score_spread":0.2360821051690463,"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."}}