{"id":"W3215499360","doi":"10.32920/ryerson.14663766.v1","title":"Towards Improved Medical Image Segmentation Using Deep Learning","year":2021,"lang":"en","type":"preprint","venue":"","topic":"AI in cancer detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Segmentation; Computer science; Artificial intelligence; Medical imaging; Generalizability theory; Convolutional neural network; Deep learning; Modalities; Modular design; Machine learning; Image segmentation; Benchmark (surveying); Pattern recognition (psychology); Contouring; Modality (human–computer interaction); Market segmentation","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":[0.001589593,0.001757579,0.001548905,0.002509582,0.0005397942,0.002429032,0.00246111,0.003051008,0.002649879],"category_scores_gemma":[0.004257778,0.0009866467,0.001943855,0.002033198,0.001132615,0.002992327,0.002829247,0.003271155,0.002339934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002021283,"about_ca_system_score_gemma":0.001643758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008595524,"about_ca_topic_score_gemma":0.01156794,"domain_scores_codex":[0.9991913,0.0001496166,0.00004672693,0.0002954704,0.0002226502,0.00009427324],"domain_scores_gemma":[0.999025,0.0003611719,0.0001348155,0.0002202629,0.0001970346,0.00006176569],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002529416,0.0001643477,0.001485202,0.0002472089,0.000178714,0.0001102298,0.0001423557,0.4102681,0.02262035,0.01247383,0.01429558,0.5377612],"study_design_scores_gemma":[0.000005900413,0.00002386033,0.0001258215,0.00001329027,0.000009234354,0.00002761366,0.000009205866,0.9873991,0.003047961,0.007984047,0.001347481,0.000006458396],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01440657,0.001028094,0.9769345,0.0008685492,0.00007673502,0.00007121997,0.0005275194,0.004617498,0.001469378],"genre_scores_gemma":[0.2273088,0.001157921,0.7582801,0.001361752,0.0002421994,0.0001836038,0.004187839,0.0009798438,0.006297919],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008595524,"threshold_uncertainty_score":0.01709098,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02154747272785901,"score_gpt":0.3054641561671573,"score_spread":0.2839166834392983,"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."}}