{"id":"W2071096105","doi":"10.1006/rtim.2001.0285","title":"Multiresolution-Based Segmentation of Calcifications for the Early Detection of Breast Cancer","year":2002,"lang":"en","type":"article","venue":"Real-Time Imaging","topic":"AI in cancer detection","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"University of South Florida; BC Cancer Agency; University of Florida","keywords":"Segmentation; Artificial intelligence; Mammography; Pixel; Computer science; Computer vision; Wavelet; Pattern recognition (psychology); Multiresolution analysis; Contrast (vision); Digital mammography; Fuzzy logic; CAD; Breast cancer; Wavelet transform; Discrete wavelet transform; Cancer; Medicine; Engineering","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":[0.0009502355,0.0004232006,0.0005172764,0.0024862,0.0002426654,0.001007354,0.0006286785,0.000955472,0.001627657],"category_scores_gemma":[0.003156404,0.0005973052,0.0006597092,0.001011074,0.0002830769,0.0006951963,0.0004844938,0.0007685679,0.0005649533],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002718191,"about_ca_system_score_gemma":0.0003969489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001361932,"about_ca_topic_score_gemma":0.001429948,"domain_scores_codex":[0.9996451,0.00009197873,0.00002196948,0.00004361094,0.0001517573,0.00004562623],"domain_scores_gemma":[0.9992397,0.000387842,0.0001004478,0.0001020492,0.000116057,0.00005404318],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005668193,0.00008062986,0.002049545,0.0005091901,0.0001126254,0.0002996217,0.0001715099,0.01592677,0.6507203,0.002120337,0.001403863,0.3260387],"study_design_scores_gemma":[0.00008777647,0.0002522529,0.01634947,0.0001190368,0.0003624704,0.004087891,0.0000933121,0.6239696,0.3423656,0.003673929,0.008548256,0.0000904154],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1241944,0.00908071,0.8612881,0.0005149147,0.00007735762,0.00009135763,0.0002330968,0.002015948,0.00250417],"genre_scores_gemma":[0.3592605,0.003328228,0.6346954,0.0001580446,0.00008816931,0.00004444871,0.0002152208,0.000350624,0.001859288],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0024862,"threshold_uncertainty_score":0.005445063,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01760683202522484,"score_gpt":0.2664991107505257,"score_spread":0.2488922787253009,"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."}}