{"id":"W4367848900","doi":"10.32920/22734299","title":"Machine Learning on Biomedical Images: Interactive Learning, Transfer Learning, Class Imbalance, and Beyond","year":2023,"lang":"en","type":"preprint","venue":"","topic":"AI in cancer detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Transfer of learning; Artificial intelligence; Segmentation; Machine learning; Rendering (computer graphics); Volume rendering; Class (philosophy); Limit (mathematics); Training set; Mathematics","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":[0.006437126,0.0008807573,0.0008730961,0.001004096,0.0005502798,0.00230946,0.001624923,0.001850688,0.002548287],"category_scores_gemma":[0.0215937,0.0002969344,0.0004935177,0.001340331,0.002354372,0.003923204,0.00290047,0.003117493,0.0006694238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001591073,"about_ca_system_score_gemma":0.0007712388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001893816,"about_ca_topic_score_gemma":0.001258804,"domain_scores_codex":[0.9976988,0.001074894,0.00006876407,0.0003618265,0.0006196462,0.0001760496],"domain_scores_gemma":[0.9895746,0.007591202,0.0004663939,0.001381553,0.0006512172,0.0003350264],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005717695,0.0003787371,0.008035941,0.0003372231,0.0001186359,0.0004720771,0.0005110379,0.2153764,0.01171084,0.06634952,0.01151671,0.6846212],"study_design_scores_gemma":[0.00002931222,0.0001471109,0.002397009,0.00004853071,0.00001316742,0.0002336859,0.0001511996,0.8617183,0.01067229,0.1191878,0.005375927,0.00002561685],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.1032811,0.005082933,0.8660581,0.0121707,0.0003272844,0.000147259,0.0002658404,0.002175875,0.01049093],"genre_scores_gemma":[0.7968588,0.001749787,0.1938816,0.0009449197,0.0005756232,0.0001725118,0.0003302708,0.0003261174,0.005160296],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.006437126,"threshold_uncertainty_score":0.03404319,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01542701111076221,"score_gpt":0.2705299063062502,"score_spread":0.255102895195488,"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."}}