{"id":"W3105158752","doi":"10.1007/s12553-020-00506-6","title":"Automatic suspicions lesions segmentation based on variable-size windows in mammography images","year":2020,"lang":"en","type":"article","venue":"Health and Technology","topic":"AI in cancer detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Thresholding; False positive paradox; Mammography; Segmentation; Computer science; Artificial intelligence; Histogram; Pixel; Pattern recognition (psychology); False positives and false negatives; Variable (mathematics); Computer vision; Breast cancer; Image (mathematics); Mathematics; 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":[0.0004183858,0.0003863101,0.0003740049,0.001776665,0.0002051766,0.001048106,0.0003768238,0.0005630377,0.0009266796],"category_scores_gemma":[0.001259054,0.0003064286,0.0004535757,0.0006365457,0.0002120753,0.0005057556,0.0003511528,0.0002930108,0.0003113392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002447592,"about_ca_system_score_gemma":0.0004867984,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002344332,"about_ca_topic_score_gemma":0.002456253,"domain_scores_codex":[0.9997523,0.00003464375,0.00002129261,0.00006834375,0.00007186249,0.00005144615],"domain_scores_gemma":[0.9996452,0.0001234013,0.00005037223,0.00003235647,0.0001070158,0.00004162311],"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":[0.001970984,0.0001298068,0.02923694,0.0003395738,0.000130643,0.0007489281,0.0002804972,0.008009132,0.4550538,0.001459342,0.002112762,0.5005277],"study_design_scores_gemma":[0.00007862501,0.0004270715,0.1543213,0.000136273,0.0004490952,0.002765029,0.0004025713,0.5014296,0.3303086,0.002032205,0.007556247,0.00009332321],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7415142,0.003170438,0.2501055,0.0002801411,0.0001115097,0.0001440249,0.0005440402,0.001480876,0.002649325],"genre_scores_gemma":[0.9138606,0.0009405206,0.08319188,0.00005362531,0.00006349753,0.00003380797,0.0003936694,0.00009777139,0.001364684],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002344332,"threshold_uncertainty_score":0.004661322,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01362925393999543,"score_gpt":0.2646073776066826,"score_spread":0.2509781236666871,"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."}}