{"id":"W4365515993","doi":"10.21203/rs.3.rs-2618116/v1","title":"VOLTA: an Environment-Aware Contrastive Cell Representation Learning for Histopathology","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"AI in cancer detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Cancer Agency; University of British Columbia","funders":"","keywords":"Histopathology; Computer science; Artificial intelligence; Representation (politics); Machine learning; Sample (material); Supervised learning; Pattern recognition (psychology); Pathology; Artificial neural network; Medicine","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":[0.0007832895,0.0006306674,0.0007758625,0.0008055335,0.0003494041,0.001046947,0.002466071,0.001455499,0.001612028],"category_scores_gemma":[0.002056398,0.0004715507,0.001155943,0.0006685312,0.0006678012,0.001131624,0.001774913,0.00154935,0.0007409906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008471516,"about_ca_system_score_gemma":0.0008231414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003445518,"about_ca_topic_score_gemma":0.005486399,"domain_scores_codex":[0.9997626,0.00005630871,0.000009133736,0.00008141191,0.0000560879,0.00003436926],"domain_scores_gemma":[0.9994895,0.0002242153,0.00006147231,0.00008706626,0.00008589424,0.00005177904],"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":[0.0002243752,0.000230522,0.004899071,0.0001128662,0.0001560805,0.0001820991,0.0001192789,0.5936128,0.02625621,0.0121689,0.009138797,0.352899],"study_design_scores_gemma":[0.000005171255,0.00002451894,0.0001966553,0.000004665877,0.000006578279,0.00003319432,0.00000550651,0.9933329,0.001572286,0.004085963,0.000726729,0.000005928166],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02695804,0.0006122384,0.9678238,0.0004402952,0.0001069154,0.00006789401,0.0004092415,0.002362544,0.001218942],"genre_scores_gemma":[0.5218715,0.0007937318,0.4658012,0.0008838161,0.0002372414,0.0003392886,0.001956281,0.0003700635,0.007746931],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003445518,"threshold_uncertainty_score":0.006850898,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1357180966913737,"score_gpt":0.4130534133261471,"score_spread":0.2773353166347735,"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."}}