{"id":"W4380355035","doi":"10.1016/j.neunet.2023.06.013","title":"A multi-modal deep neural network for multi-class liver cancer diagnosis","year":2023,"lang":"en","type":"article","venue":"Neural Networks","topic":"AI in cancer detection","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer science; Deep learning; Artificial neural network; Pattern recognition (psychology); Convolutional neural network; Benchmark (surveying); Convolution (computer science); Liver cancer; Contextual image classification; Radiology; Cancer; Image (mathematics); Medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007084419,0.0007644304,0.0007195309,0.0007766659,0.000467794,0.0006683414,0.001533643,0.001546336,0.003587219],"category_scores_gemma":[0.001103171,0.0004298237,0.0008604968,0.0006413981,0.0002342064,0.0008831359,0.001332458,0.001446991,0.001134893],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006694305,"about_ca_system_score_gemma":0.0009232162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00918377,"about_ca_topic_score_gemma":0.01476645,"domain_scores_codex":[0.9997265,0.00004281936,0.00001405366,0.00008111247,0.00007210599,0.0000634301],"domain_scores_gemma":[0.9996496,0.0001078017,0.00002831702,0.00003567216,0.0001435536,0.00003496686],"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":[0.0004023204,0.0003081591,0.002733459,0.0001446676,0.0001932974,0.0001612499,0.0000571641,0.1507709,0.02158719,0.003837295,0.01389318,0.8059111],"study_design_scores_gemma":[0.000005858217,0.0000280842,0.0003216685,0.000006906097,0.00001889246,0.00003493442,0.000006753874,0.9949843,0.00259664,0.001315655,0.0006735302,0.000006882316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03237851,0.001204123,0.9588036,0.0006538911,0.0002274164,0.000115447,0.0006385671,0.00269306,0.003285369],"genre_scores_gemma":[0.5947013,0.0006659286,0.3879123,0.0009543691,0.0002409955,0.0002447688,0.001477255,0.0001679451,0.01363512],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00918377,"threshold_uncertainty_score":0.0182606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05427200641567054,"score_gpt":0.3021354614690335,"score_spread":0.247863455053363,"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."}}