{"id":"W3092537323","doi":"10.1007/978-3-030-59830-3_63","title":"A Survey on Peripheral Blood Smear Analysis Using Deep Learning","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Digital Imaging for Blood Diseases","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Context (archaeology); Peripheral blood; Deep learning; Artificial neural network; Artificial intelligence; Process (computing); Machine learning; Medicine; Immunology","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":[0.0008966117,0.0005859022,0.0006759501,0.001823431,0.0001211463,0.0006760203,0.000970269,0.0005930643,0.003779178],"category_scores_gemma":[0.002108903,0.0003774858,0.0006879091,0.002144511,0.000144437,0.001417556,0.0005862151,0.0006120796,0.001840952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002793915,"about_ca_system_score_gemma":0.0006483179,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002049047,"about_ca_topic_score_gemma":0.002864528,"domain_scores_codex":[0.999577,0.00006468096,0.000051064,0.0001191648,0.0001572654,0.00003073666],"domain_scores_gemma":[0.9991243,0.0004990502,0.00004478876,0.00006416091,0.0002298015,0.0000379596],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005543071,0.0000622576,0.002051405,0.0009134627,0.00005472811,0.00003637117,0.00001153577,0.001654616,0.001686737,0.0008151241,0.01507022,0.9775882],"study_design_scores_gemma":[0.00007270355,0.0008824333,0.02655394,0.004611714,0.0007802307,0.003931865,0.0002406182,0.1416356,0.03536198,0.01925126,0.7664599,0.0002178925],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.01912934,0.7743042,0.1830072,0.002225447,0.001195619,0.0001085751,0.001950743,0.001598063,0.01648087],"genre_scores_gemma":[0.09287628,0.7601576,0.1104441,0.002827134,0.001949804,0.000122569,0.007871312,0.0004045724,0.02334653],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003779178,"threshold_uncertainty_score":0.01264262,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.024419705581244,"score_gpt":0.2557368941255925,"score_spread":0.2313171885443485,"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."}}