{"id":"W4282934193","doi":"10.1109/jbhi.2022.3181531","title":"Multi-Magnification Image Search in Digital Pathology","year":2022,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; McMaster University; University of Waterloo","funders":"","keywords":"Magnification; Digital pathology; Computer science; Workflow; Artificial intelligence; Representation (politics); Feature (linguistics); Computer vision; Automatic image annotation; Visualization; Digital image; Feature vector; Pattern recognition (psychology); Information retrieval; Image retrieval; Image processing; Image (mathematics)","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":[0.001866739,0.00005888403,0.0001690315,0.0003160389,0.0001285953,0.00007458384,0.0003863877,0.00003283918,0.000008365362],"category_scores_gemma":[0.00004166421,0.00004687911,0.00003067285,0.0003989171,0.00009979544,0.0006261382,0.0001177202,0.0004023962,0.000004019458],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001055317,"about_ca_system_score_gemma":0.0003287078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003894351,"about_ca_topic_score_gemma":2.391668e-7,"domain_scores_codex":[0.998367,0.0000478411,0.0009145968,0.00005436058,0.0004291513,0.0001870463],"domain_scores_gemma":[0.9991572,0.00004506483,0.0004085805,0.0001134518,0.0001046049,0.0001710702],"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":[0.00003225983,0.0007252186,0.0008575448,0.0004087615,0.000009926533,0.00008177099,0.01246177,0.000006103413,0.001232337,0.002452796,0.004127176,0.9776043],"study_design_scores_gemma":[0.009563269,0.0111606,0.06514133,0.0004144097,0.00001553616,0.009613615,0.0121527,0.6757285,0.004521852,0.01131403,0.1992294,0.001144813],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01902882,0.0002070507,0.9695178,0.0108166,0.0002324718,0.0001148746,0.00001041913,0.00002325506,0.00004870675],"genre_scores_gemma":[0.748252,0.001565443,0.2470617,0.002919169,0.0001002918,0.00001059232,0.000009749579,0.000006993286,0.00007405598],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9764595,"threshold_uncertainty_score":0.1911675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04712505580705458,"score_gpt":0.3391377065918728,"score_spread":0.2920126507848182,"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."}}