{"id":"W4408688446","doi":"10.1016/j.compbiomed.2025.110003","title":"CACTUS: An open dataset and framework for automated Cardiac Assessment and Classification of Ultrasound images using deep transfer learning","year":2025,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut national de psychiatrie légale Philippe-Pinel; Université Laval; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Transfer of learning; Convolutional neural network; Deep learning; Computer science; Artificial intelligence; Field (mathematics); Component (thermodynamics); Imaging phantom; Machine learning; Artificial neural network; Cardiac imaging; Medicine; Radiology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001489669,0.004076618,0.001805875,0.005515878,0.0009121108,0.002155023,0.006491516,0.004307528,0.009229859],"category_scores_gemma":[0.004841052,0.0007887332,0.002264823,0.003264301,0.0008249026,0.001465073,0.002985667,0.002846608,0.01069235],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002400519,"about_ca_system_score_gemma":0.002568129,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02977079,"about_ca_topic_score_gemma":0.05117943,"domain_scores_codex":[0.9985479,0.0001805703,0.0001618115,0.0003863157,0.0005325813,0.0001909053],"domain_scores_gemma":[0.9985288,0.0003615733,0.0001725286,0.0003586868,0.0003451117,0.0002333237],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001434229,0.001176422,0.006927769,0.002418099,0.0004788967,0.001099505,0.0001647469,0.0159389,0.007958498,0.002350981,0.78962,0.170432],"study_design_scores_gemma":[0.002045855,0.001089209,0.03442296,0.001577083,0.0003532451,0.003743002,0.0004912977,0.2101488,0.02352457,0.01160461,0.7103384,0.0006608833],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.03293413,0.005686644,0.03148151,0.001523128,0.001059824,0.001972257,0.8536395,0.0652759,0.006427001],"genre_scores_gemma":[0.02231087,0.0008240004,0.03000886,0.0004266171,0.0001052823,0.001239093,0.9421216,0.0007862655,0.002177446],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02977079,"threshold_uncertainty_score":0.05919504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03647597308855118,"score_gpt":0.4283096102360709,"score_spread":0.3918336371475198,"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."}}