{"id":"W4393447018","doi":"10.5281/zenodo.7393060","title":"Datasets of \"Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation\" Part 6 of 14","year":2022,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Contrast (vision); Texture (cosmology); Segmentation; Artificial intelligence; Pattern recognition (psychology); Net (polyhedron); Computer science; Brain tissue; Image (mathematics); Psychology; Mathematics; Neuroscience; Geometry","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":[0.002274279,0.003591935,0.001621561,0.002086969,0.001087957,0.001708705,0.004946195,0.004022362,0.0250717],"category_scores_gemma":[0.007172052,0.000712538,0.003106653,0.001819924,0.0008973868,0.001017816,0.001951432,0.002942373,0.01697757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002112971,"about_ca_system_score_gemma":0.001652751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01783934,"about_ca_topic_score_gemma":0.03501698,"domain_scores_codex":[0.9985874,0.0003800023,0.0001313809,0.0003705991,0.0003469629,0.0001836389],"domain_scores_gemma":[0.9961636,0.001388058,0.0002252869,0.001019578,0.0009207783,0.0002827001],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00146727,0.0009507893,0.005317665,0.001806173,0.0005446662,0.0004603945,0.0001011036,0.01943105,0.002235798,0.001163099,0.9232055,0.04331645],"study_design_scores_gemma":[0.003686299,0.001930604,0.09062816,0.001793888,0.0006775709,0.003116665,0.0008191162,0.2221785,0.02001275,0.01957953,0.6349995,0.0005774216],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.05410327,0.003791674,0.00833186,0.001972618,0.001299376,0.0009363134,0.9122058,0.009100756,0.008258337],"genre_scores_gemma":[0.03065783,0.0005059219,0.00698987,0.000397824,0.0001128823,0.0007755653,0.9564683,0.0004166806,0.003675089],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0250717,"threshold_uncertainty_score":0.08387321,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04368694523188586,"score_gpt":0.2752724323362534,"score_spread":0.2315854871043675,"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."}}