{"id":"W4403285678","doi":"10.21203/rs.3.rs-5028318/v1","title":"MedMAE: A Self-Supervised Backbone for Medical Imaging Tasks","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"AI in cancer detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Autoencoder; Medical imaging; Artificial intelligence; Task (project management); Deep learning; Domain (mathematical analysis); Machine learning; Code (set theory); Backbone network; Representation (politics); 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":[0.001866039,0.001575678,0.001506951,0.001853261,0.0009503429,0.001482297,0.003853511,0.002762953,0.0156498],"category_scores_gemma":[0.005994656,0.001286292,0.00152285,0.001273852,0.0007020098,0.003185018,0.003526108,0.003392929,0.01129414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006123806,"about_ca_system_score_gemma":0.00143592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002467712,"about_ca_topic_score_gemma":0.00616696,"domain_scores_codex":[0.9991296,0.0001920019,0.00003780387,0.0003486513,0.000207714,0.00008428434],"domain_scores_gemma":[0.9980299,0.0005776464,0.00009025394,0.0007099158,0.0003942258,0.0001980077],"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":[0.001119218,0.0007492531,0.001580516,0.0002979488,0.000324211,0.0001303477,0.0001101039,0.06249772,0.01583064,0.008416599,0.1100317,0.7989119],"study_design_scores_gemma":[0.00009441841,0.0001301883,0.000577539,0.00003237293,0.00003333325,0.0001110249,0.00002032391,0.9410667,0.01177823,0.02615852,0.01996933,0.00002791972],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007474294,0.0005249791,0.9455958,0.0004430052,0.0003044274,0.000244407,0.002569277,0.03976718,0.003076578],"genre_scores_gemma":[0.103085,0.0004022896,0.8626692,0.0009369709,0.0003395301,0.0007268594,0.01126054,0.004112917,0.01646668],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0156498,"threshold_uncertainty_score":0.0523538,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04915794766473349,"score_gpt":0.4094112989268251,"score_spread":0.3602533512620916,"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."}}