{"id":"W4391610047","doi":"10.1080/09500340.2024.2313724","title":"Breast mass regions classification from mammograms using convolutional neural networks and transfer learning.","year":2023,"lang":"en","type":"article","venue":"Journal of Modern Optics","topic":"AI in cancer detection","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Government Council on Grants, Russian Federation","keywords":"Artificial intelligence; Mammography; Computer science; Convolutional neural network; Pattern recognition (psychology); Digital mammography; Segmentation; Transfer of learning; Artificial neural network; Region of interest; Deep learning; Intersection (aeronautics); Breast cancer; Sørensen–Dice coefficient; Image quality; Image segmentation; Computer vision; Image (mathematics); Cancer; Medicine; Cartography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002664833,0.000094454,0.0001394001,0.0001214297,0.0001550713,0.0001380202,0.0002477462,0.00008938123,0.000001950749],"category_scores_gemma":[0.00001136224,0.00009113109,0.00007048216,0.0002931075,0.00005912895,0.0004615832,0.00003978104,0.0003980541,0.000001369315],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001150733,"about_ca_system_score_gemma":0.00006191987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001138503,"about_ca_topic_score_gemma":0.000004556437,"domain_scores_codex":[0.9989687,0.00007683491,0.0002989844,0.0001574997,0.0003202209,0.000177722],"domain_scores_gemma":[0.999321,0.0001068531,0.0001526854,0.0001395496,0.0001824588,0.00009741157],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003781872,0.0000181764,0.002597475,0.000004847265,0.00004854178,0.00002281552,0.0003577658,0.9430162,0.003000529,0.001385136,0.00007944665,0.04943126],"study_design_scores_gemma":[0.0002967601,0.00006148723,0.01059988,0.00002688485,0.0000259799,0.0003990938,0.00004850964,0.9836355,0.00002323117,0.004726215,0.00006617812,0.00009031194],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2351243,0.0001783231,0.7632954,0.0009004527,0.0004092855,0.0000346512,0.000002206272,0.00004304388,0.00001227212],"genre_scores_gemma":[0.9842811,0.0001940952,0.01507962,0.00003573936,0.0003653837,9.44395e-7,0.000002675348,0.00001210883,0.00002828624],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7491568,"threshold_uncertainty_score":0.3716218,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04760489988587305,"score_gpt":0.2601427027437144,"score_spread":0.2125378028578414,"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."}}