{"id":"W4256411336","doi":"10.1101/2021.06.29.448970","title":"MICRA-Net: MICRoscopy Analysis Neural Network to solve detection, classification, and segmentation from a single simple auxiliary task","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute; Canadian Institute for Advanced Research; Université Laval","funders":"Fonds de recherche du Québec – Nature et technologies; Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research; National Science Foundation","keywords":"Computer science; Artificial intelligence; Segmentation; Task (project management); Artificial neural network; Pattern recognition (psychology); Feature (linguistics); Annotation; Process (computing); Machine learning; Data mining","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002908896,0.0004969693,0.0005690121,0.000260765,0.0002328228,0.0005437146,0.000359649,0.0005359947,0.00002735554],"category_scores_gemma":[0.0001301306,0.0006043789,0.0002935117,0.0009452293,0.0001069678,0.00001925698,0.0006212394,0.000315499,0.000005441011],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001380352,"about_ca_system_score_gemma":0.0001615652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003798329,"about_ca_topic_score_gemma":0.000307242,"domain_scores_codex":[0.9970504,0.0001606939,0.0005923057,0.001546003,0.0002266267,0.0004239633],"domain_scores_gemma":[0.9972504,0.00002725015,0.0004131098,0.0014695,0.0005974197,0.0002423329],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003417264,0.00008927187,0.01844363,0.00003415863,0.001008727,0.000008226007,0.00001262848,0.0004621355,0.9788203,6.259785e-7,0.001070749,0.00001542962],"study_design_scores_gemma":[0.0001731959,0.0000644075,0.06832154,0.00003149406,0.001099853,1.846043e-8,0.00001306001,0.00145638,0.9256465,0.000001408804,0.002613576,0.0005785693],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8865005,0.001899318,0.1107272,0.00008431412,0.0001228667,0.0004500221,0.00009981608,0.0001138359,0.00000214029],"genre_scores_gemma":[0.9664175,0.0003145965,0.03170129,0.0006818955,0.0005265521,0.0001997353,0.00006329926,0.00009063483,0.000004518606],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07991696,"threshold_uncertainty_score":0.9996408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009068668856190556,"score_gpt":0.2386379390304612,"score_spread":0.2295692701742706,"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."}}