{"id":"W4392667554","doi":"10.1007/978-3-031-55088-1_7","title":"Self Supervised Multi-view Graph Representation Learning in Digital Pathology","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"AI in cancer detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Sunnybrook Health Science Centre","funders":"","keywords":"Computer science; Digital pathology; Graph; Economic shortage; Artificial intelligence; Representation (politics); Machine learning; External Data Representation; Deep learning; Labeled data; Theoretical computer science; Pattern recognition (psychology)","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":[0.0006419509,0.000564569,0.0009720409,0.001549408,0.0002567676,0.0009843286,0.001491025,0.001272673,0.001738752],"category_scores_gemma":[0.001535914,0.0004539105,0.001276935,0.001604916,0.0004665725,0.001289294,0.00109845,0.001147803,0.001014094],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004991136,"about_ca_system_score_gemma":0.0004062399,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002676773,"about_ca_topic_score_gemma":0.004753964,"domain_scores_codex":[0.9996061,0.00009928527,0.00001530942,0.000142813,0.00009560618,0.00004104354],"domain_scores_gemma":[0.9992276,0.0003420796,0.00007611921,0.0001796247,0.000132223,0.00004232674],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000135715,0.0001571289,0.001342153,0.0002691837,0.0001769093,0.0001159983,0.00009280569,0.1605648,0.02043422,0.0077455,0.01455503,0.7944106],"study_design_scores_gemma":[0.000003995551,0.00004045343,0.0005394712,0.00001418457,0.00002544798,0.0001418042,0.00002053567,0.9861009,0.003841704,0.007259653,0.002002618,0.000009354354],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02094142,0.00161446,0.9736867,0.0002948829,0.00008196229,0.00003613513,0.0003267998,0.001610634,0.001407061],"genre_scores_gemma":[0.4496146,0.002384665,0.5316147,0.0003606613,0.000276218,0.0001117861,0.002788068,0.0006249982,0.01222424],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002676773,"threshold_uncertainty_score":0.005816698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02083813577045536,"score_gpt":0.2663891553355021,"score_spread":0.2455510195650467,"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."}}