{"id":"W7131894055","doi":"10.48620/93777","title":"Polarimetry-based tumor segmentation for pancreatic cancer using film-covered hematoxylin-and-eosin-stained slides","year":2025,"lang":"en","type":"article","venue":"Open Access CRIS of the University of Bern","topic":"Optical Polarization and Ellipsometry","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Pancreatic cancer; Segmentation; Cancer; Pancreatic disease; Image segmentation; Pancreas","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":[0.00008863694,0.00008067102,0.0001979813,0.0001156079,0.0001356035,0.00006941755,0.0005603823,0.00003848721,0.00006757025],"category_scores_gemma":[0.00004430172,0.00007904233,0.00006018087,0.0003395767,0.00006113796,0.0004015414,0.0002415369,0.00005162087,5.295128e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007724893,"about_ca_system_score_gemma":0.00005687539,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001942574,"about_ca_topic_score_gemma":0.00008369307,"domain_scores_codex":[0.9995631,0.00002116594,0.0001284071,0.00010378,0.00008426615,0.00009929093],"domain_scores_gemma":[0.9995854,0.00007457686,0.00008018591,0.0001551477,0.00007950327,0.0000251498],"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":[0.001282409,0.0004749487,0.3897462,0.007754358,0.001498412,0.000003859751,0.001425909,0.4067059,0.1605034,0.006899888,0.01219985,0.01150491],"study_design_scores_gemma":[0.004145663,0.00004239633,0.04397224,0.0009014684,0.0007755117,5.740588e-7,0.001738758,0.5774671,0.367544,0.001251519,0.001749094,0.0004116686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8973252,0.0001871219,0.100037,0.0003596481,0.0001507325,0.0005735905,0.0001389078,0.00002533802,0.00120251],"genre_scores_gemma":[0.9913046,0.00001670217,0.008012593,0.0001032161,0.000004792462,3.418273e-7,0.0000086898,0.0000091068,0.0005400061],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3457739,"threshold_uncertainty_score":0.3223253,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0270167781238407,"score_gpt":0.3134255842947101,"score_spread":0.2864088061708694,"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."}}