{"id":"W2159013813","doi":"10.1007/s10278-013-9587-6","title":"Automatic Detection of the Nipple in Screen-Film and Full-field Digital Mammograms Using a Novel Hessian-Based Method","year":2013,"lang":"en","type":"article","venue":"Journal of Digital Imaging","topic":"AI in cancer detection","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Universidade de São Paulo","keywords":"Hessian matrix; Artificial intelligence; Mammography; Digital mammography; Computer vision; Computer science; Pixel; Distortion (music); Pattern recognition (psychology); Mathematics; Breast cancer; 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":[0.0003120129,0.0004063271,0.0005134524,0.001052075,0.000234279,0.0004373403,0.0005939925,0.0004539982,0.002035392],"category_scores_gemma":[0.0007170109,0.0003183739,0.0003559622,0.0004384763,0.0001681364,0.0004998606,0.0004123338,0.0002331163,0.0007111463],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002136723,"about_ca_system_score_gemma":0.0006332615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003627334,"about_ca_topic_score_gemma":0.009338149,"domain_scores_codex":[0.9997845,0.00002737333,0.00001409727,0.0000490225,0.000102521,0.00002247987],"domain_scores_gemma":[0.9995942,0.0001309556,0.00003451129,0.00002476336,0.0001766687,0.00003882826],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004240538,0.0001948867,0.004687918,0.0002411936,0.00009493047,0.0001879503,0.00006261842,0.00817237,0.3393919,0.00109824,0.002672094,0.6427718],"study_design_scores_gemma":[0.00006623911,0.000184935,0.02773847,0.00002235639,0.0001000402,0.001250734,0.0000584002,0.8961298,0.07062314,0.000739485,0.003014939,0.00007145747],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1120406,0.000614993,0.8834726,0.0001633911,0.00005694691,0.0000959551,0.0003039436,0.00168795,0.001563585],"genre_scores_gemma":[0.366453,0.000445492,0.6276058,0.00008111293,0.00005004863,0.00006238569,0.0005505329,0.0001591498,0.004592413],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003627334,"threshold_uncertainty_score":0.00721246,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01341263259708858,"score_gpt":0.2588320018610167,"score_spread":0.2454193692639282,"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."}}