{"id":"W4393182497","doi":"10.4108/eetpht.10.5549","title":"Deep Learning in Medical Imaging: A Case Study on Lung Tissue Classification","year":2024,"lang":"en","type":"article","venue":"EAI Endorsed Transactions on Pervasive Health and Technology","topic":"AI in cancer detection","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wycliffe College","funders":"","keywords":"Artificial intelligence; Medical imaging; Lung; Computer science; Computer vision; Medicine; Internal medicine","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":[0.0003799728,0.0001742321,0.0002131359,0.001234385,0.0003591508,0.00006996105,0.000235761,0.0001615053,0.0000263492],"category_scores_gemma":[0.00005582493,0.0001729191,0.00002847573,0.001481719,0.00009602427,0.0002081232,0.00001270423,0.001223765,0.00002588131],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003441123,"about_ca_system_score_gemma":0.0003764918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002994931,"about_ca_topic_score_gemma":0.0008515063,"domain_scores_codex":[0.9981285,0.0001839712,0.0003431478,0.0006977516,0.0002829584,0.0003636388],"domain_scores_gemma":[0.9991367,0.0002263256,0.00005777572,0.0003540747,0.0000454658,0.0001796943],"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":[0.0000141392,0.0002295963,0.000690978,0.00006250932,0.00001746618,0.002170436,0.002915143,0.0001479005,0.0000413665,0.002555341,0.00003151627,0.9911236],"study_design_scores_gemma":[0.002636216,0.004162747,0.001393782,0.00041786,0.00002728668,0.01382578,0.02461515,0.9362655,0.0008653434,0.001565318,0.01363626,0.0005887584],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05083714,0.001677665,0.9027972,0.04183291,0.0007710022,0.0007687087,0.000001671203,0.001177364,0.0001363305],"genre_scores_gemma":[0.9980458,0.0003364953,0.0007455753,0.0004518851,0.000031303,0.0003120679,0.000001219357,0.0000185482,0.00005712231],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9905348,"threshold_uncertainty_score":0.7051434,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01737770767857643,"score_gpt":0.3273087735041327,"score_spread":0.3099310658255563,"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."}}