{"id":"W4293571963","doi":"10.1016/j.compbiomed.2022.106004","title":"Domain-aware contrastive learning for ultrasound hip image analysis","year":2022,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Hip disorders and treatments","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Ultrasound; Artificial intelligence; Translation (biology); Similarity (geometry); Image quality; Cosine similarity; 3D ultrasound; Computer vision; Software portability; Image (mathematics); Pattern recognition (psychology); Radiology; Medicine","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":[0.0008194295,0.000629433,0.0007024663,0.0009244437,0.0002740559,0.0006446381,0.001079422,0.0008590926,0.002047162],"category_scores_gemma":[0.002301534,0.0002032808,0.000829845,0.0007228847,0.000306767,0.000712187,0.001116442,0.001244443,0.0009354602],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004611293,"about_ca_system_score_gemma":0.0009364554,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004244966,"about_ca_topic_score_gemma":0.005883461,"domain_scores_codex":[0.9996479,0.00008944271,0.00002054134,0.0001019532,0.00008524833,0.00005509686],"domain_scores_gemma":[0.99913,0.0004411366,0.00005113768,0.0001176686,0.0002156402,0.0000446318],"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":[0.0003916614,0.0004137605,0.002042073,0.0001795407,0.0001138278,0.0001369384,0.00006611134,0.08631529,0.03580763,0.00431828,0.007382807,0.862832],"study_design_scores_gemma":[0.00001918396,0.0001062116,0.0009193106,0.00001781103,0.00003064972,0.0001263591,0.0000297798,0.9774465,0.01334462,0.005684978,0.002264038,0.00001062524],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03686283,0.001378894,0.956738,0.0002965865,0.00008085389,0.00008234329,0.0004161815,0.002336596,0.001807689],"genre_scores_gemma":[0.4831131,0.0008367174,0.5096199,0.0003365952,0.0001237419,0.0001195999,0.001615873,0.0001716247,0.004062906],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004244966,"threshold_uncertainty_score":0.008440495,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0121122959290676,"score_gpt":0.3203051997223947,"score_spread":0.3081929037933271,"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."}}