{"id":"W4396590223","doi":"10.1007/s11517-024-03084-1","title":"BraNet: a mobil application for breast image classification based on deep learning algorithms","year":2024,"lang":"en","type":"article","venue":"Medical & Biological Engineering & Computing","topic":"AI in cancer detection","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Universitat Politècnica de València","keywords":"Artificial intelligence; Computer science; Mammography; Breast ultrasound; Segmentation; Machine learning; Cohen's kappa; Deep learning; Medical diagnosis; Python (programming language); Breast imaging; Contextual image classification; Confusion matrix; Medical imaging; Algorithm; Breast cancer; Medicine; Image (mathematics); Radiology; Cancer","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":[0.0003596596,0.001124343,0.00050292,0.001186016,0.000161479,0.0006873371,0.001463965,0.0008032848,0.01768507],"category_scores_gemma":[0.001529593,0.0005096309,0.0007062881,0.0004384859,0.0002105855,0.0007575561,0.001467933,0.0007425953,0.005537733],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003188906,"about_ca_system_score_gemma":0.0003963588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001074644,"about_ca_topic_score_gemma":0.001984294,"domain_scores_codex":[0.9997817,0.00002792746,0.00001716052,0.00006100571,0.00008540321,0.0000267616],"domain_scores_gemma":[0.9997568,0.0001266179,0.00002172107,0.00002927549,0.00004847896,0.00001711286],"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":[0.001668539,0.0003252897,0.004431139,0.00133399,0.0003519677,0.001429314,0.0003630245,0.02562146,0.05292348,0.00464844,0.1212808,0.7856225],"study_design_scores_gemma":[0.0003263415,0.0005081857,0.009017942,0.0003032282,0.0001429787,0.003407585,0.0001400736,0.8057271,0.0770464,0.01235749,0.09084056,0.0001820389],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04272021,0.002086298,0.6367952,0.0005159174,0.000240724,0.0007541002,0.007835791,0.2997206,0.009331102],"genre_scores_gemma":[0.3595484,0.001983133,0.5704615,0.00124594,0.0001706709,0.001798251,0.02122908,0.01625899,0.02730416],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01768507,"threshold_uncertainty_score":0.05916238,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01291744627810627,"score_gpt":0.2601910600174069,"score_spread":0.2472736137393006,"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."}}