{"id":"W4406785230","doi":"10.48550/arxiv.2501.13193","title":"Revisiting Data Augmentation for Ultrasound Images","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada","keywords":"Ultrasound; Computer vision; Computer science; Artificial intelligence; Computer graphics (images); Medicine; Radiology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003099451,0.001690998,0.001013188,0.001334067,0.0005012849,0.001789303,0.001829508,0.001774501,0.002697356],"category_scores_gemma":[0.01549925,0.0006709785,0.001821021,0.001261177,0.001642006,0.002510953,0.002796534,0.003346943,0.001675651],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000634104,"about_ca_system_score_gemma":0.001124695,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002935398,"about_ca_topic_score_gemma":0.003780033,"domain_scores_codex":[0.9980586,0.0007421791,0.0001323255,0.000489438,0.0004411269,0.000136318],"domain_scores_gemma":[0.9941989,0.002979311,0.0003832146,0.001515708,0.0007851334,0.0001377302],"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":[0.000896581,0.000428356,0.005374292,0.0009476089,0.0002898612,0.0003853644,0.0005551496,0.1911991,0.06751598,0.01059688,0.01901281,0.7027981],"study_design_scores_gemma":[0.00004065404,0.0003897774,0.0021233,0.00015096,0.00008243148,0.0003919926,0.000121605,0.9188867,0.04603121,0.01893829,0.01278925,0.00005388735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07349525,0.00293993,0.9059926,0.002103215,0.0007725828,0.0003097878,0.00189114,0.008164503,0.004331087],"genre_scores_gemma":[0.4897444,0.001391111,0.4961748,0.001220409,0.0004292284,0.0004302493,0.005337025,0.0008624149,0.004410299],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003099451,"threshold_uncertainty_score":0.01639169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1087417542689535,"score_gpt":0.3885910580925978,"score_spread":0.2798493038236443,"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."}}