{"id":"W7161975338","doi":"10.82308/18624","title":"An automated approach for segmenting regions containing invasive ductal breast carcinomas in whole digital slides","year":2015,"lang":"en","type":"dissertation","venue":"","topic":"AI in cancer detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Breast cancer; Breast tumor; Open source","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0008185094,0.001010552,0.0008793623,0.003804896,0.0008284856,0.001936873,0.001525326,0.001266391,0.003649886],"category_scores_gemma":[0.001761367,0.000671546,0.00107511,0.001872344,0.0004671167,0.0009497554,0.001050234,0.0006289866,0.002128761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008236517,"about_ca_system_score_gemma":0.001912577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0091254,"about_ca_topic_score_gemma":0.02012953,"domain_scores_codex":[0.9988135,0.0001275958,0.0000771308,0.0003908501,0.0004531543,0.0001377675],"domain_scores_gemma":[0.9990274,0.0002366228,0.0001098421,0.0001660649,0.0003983246,0.00006180119],"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":[0.0002538669,0.0001471783,0.004126457,0.0003584678,0.0001558842,0.0002864675,0.0002494512,0.00937682,0.1243294,0.001743944,0.007430622,0.8515413],"study_design_scores_gemma":[0.0001056959,0.0005639108,0.03806561,0.0001290542,0.0003101841,0.003019492,0.0006770805,0.709874,0.1873797,0.004853315,0.05481789,0.000204126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05384482,0.001421064,0.9280887,0.0003273555,0.0001585204,0.0005408946,0.001198606,0.01166034,0.002759648],"genre_scores_gemma":[0.1368472,0.0007458734,0.8501713,0.0001610256,0.00007838817,0.000338836,0.002166294,0.0004409269,0.009050224],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0091254,"threshold_uncertainty_score":0.01814461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02721759442310623,"score_gpt":0.2940765848854525,"score_spread":0.2668589904623462,"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."}}