{"id":"W2951719372","doi":"10.48550/arxiv.1610.00320","title":"Stacked Autoencoders for Medical Image Search","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Image retrieval; Artificial intelligence; Benchmark (surveying); ENCODE; Pattern recognition (psychology); Feature extraction; Content-based image retrieval; Medical imaging; Image (mathematics); Local binary patterns; Computer vision; Information retrieval; Histogram; Geography","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":[0.0006540934,0.0002508527,0.0002836713,0.0002290065,0.0001593262,0.0001414689,0.002804232,0.0003959939,0.0001040996],"category_scores_gemma":[0.0001455496,0.0002288486,0.0002573284,0.0003496348,0.0002678636,0.0003978458,0.00167634,0.000433437,0.000108694],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002321729,"about_ca_system_score_gemma":0.0007284039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002255635,"about_ca_topic_score_gemma":0.000003523512,"domain_scores_codex":[0.997938,0.0001336902,0.0002230046,0.001043375,0.0002379349,0.0004240198],"domain_scores_gemma":[0.9977726,0.0002343977,0.0001516263,0.001157851,0.0004034424,0.000280116],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007301567,0.0001749902,0.0001729312,0.0002590217,0.000117361,0.0002295835,0.0002100615,0.0001102073,0.000688671,0.979549,0.005402983,0.01301221],"study_design_scores_gemma":[0.0009144379,0.0001154414,0.0001919977,0.0002117181,0.00003740524,0.000006681796,0.00005058797,0.748179,0.01082374,0.2325302,0.006227449,0.0007113265],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001447649,0.00002409162,0.9919123,0.001985108,0.0003074793,0.0004779944,0.00003708214,0.0006669587,0.003141358],"genre_scores_gemma":[0.9642838,0.0003299993,0.02742826,0.0002645548,0.0001563704,0.000007827262,0.0000242908,0.00003160546,0.007473254],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.964484,"threshold_uncertainty_score":0.9332173,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08300237818767757,"score_gpt":0.2355329733591308,"score_spread":0.1525305951714533,"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."}}