{"id":"W4403620826","doi":"10.48550/arxiv.2409.06821","title":"Sam2Rad: A Segmentation Model for Medical Images with Learnable Prompts","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Alberta Innovates; Arthritis Society; Compute Canada; Canadian Institute for Advanced Research","keywords":"Segmentation; Computer science; Artificial intelligence; Computer vision; Computer graphics (images); Pattern recognition (psychology)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001099015,0.001304796,0.0007167141,0.0006977106,0.0002728335,0.0008027083,0.001980301,0.001656511,0.00436131],"category_scores_gemma":[0.003278946,0.0006822674,0.001182592,0.0006434075,0.0005584477,0.001615134,0.001292184,0.001970649,0.002221153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007514112,"about_ca_system_score_gemma":0.00121038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002982116,"about_ca_topic_score_gemma":0.006669853,"domain_scores_codex":[0.9996796,0.00006698811,0.00001751625,0.0001325702,0.00006933207,0.00003403697],"domain_scores_gemma":[0.999356,0.0002282107,0.00006056418,0.0001304188,0.0001706939,0.00005419792],"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":[0.0008895223,0.0001822401,0.002909304,0.0003092827,0.0001415566,0.0002650905,0.0001615573,0.3748073,0.02355097,0.00945304,0.02423492,0.5630953],"study_design_scores_gemma":[0.00002420327,0.0001007003,0.0003107094,0.00001420712,0.00001475267,0.00007089919,0.00001243952,0.9862576,0.00508725,0.00528468,0.002809459,0.00001317035],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01177675,0.0003434042,0.9673332,0.000289932,0.0001143581,0.00009548687,0.000621823,0.01845461,0.0009704419],"genre_scores_gemma":[0.3216642,0.0006093166,0.6615953,0.0008197375,0.0001977062,0.0005067129,0.003712098,0.001712829,0.009182052],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00436131,"threshold_uncertainty_score":0.01459002,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04406611789053846,"score_gpt":0.2411378471538685,"score_spread":0.1970717292633301,"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."}}