{"id":"W4409102058","doi":"10.1109/jiot.2025.3557157","title":"An Enhanced Multiscale Collaborative Learning Network for Medical Image Segmentation in Internet of Medical Things","year":2025,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Iron Ore Company (Canada)","funders":"Natural Science Foundation of Hebei Province; National Natural Science Foundation of China; National Science Foundation","keywords":"Computer science; Internet of Things; Image segmentation; Scale (ratio); The Internet; Segmentation; Artificial intelligence; Computer vision; Computer network; Machine learning; Multimedia; World Wide Web","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":[0.002167234,0.000140525,0.000313595,0.0002354383,0.00006686384,0.00009367362,0.0006473967,0.0001908253,0.0003126682],"category_scores_gemma":[0.003027873,0.0001283857,0.00009729545,0.0004278437,0.0002413146,0.0006902813,0.00005237744,0.0008554141,0.000003283624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00011678,"about_ca_system_score_gemma":0.0002273898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007935486,"about_ca_topic_score_gemma":0.00003417047,"domain_scores_codex":[0.9972429,0.0004648623,0.0008878245,0.0002960246,0.0008710566,0.0002373449],"domain_scores_gemma":[0.9982432,0.0006181994,0.0006625468,0.0001099101,0.0002212682,0.000144865],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008379662,0.0003660446,0.0007889033,0.0001136773,0.00003938813,0.00002533366,0.0136149,0.0002731648,0.9294649,0.002055493,0.003920334,0.04849988],"study_design_scores_gemma":[0.001536302,0.0003544994,0.0004009659,0.0007399523,0.00001240287,0.00005016109,0.00102262,0.1988515,0.7956251,0.000875018,0.0004266283,0.000104817],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5891547,0.00002977342,0.4072087,0.001474516,0.001226552,0.0002238235,0.000001321966,0.00003078666,0.000649835],"genre_scores_gemma":[0.9944634,0.00006511771,0.003643838,0.001061591,0.000110888,0.00002185463,0.000002638499,0.00001452436,0.0006161905],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4053086,"threshold_uncertainty_score":0.5235416,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01539829530504928,"score_gpt":0.3278617491081907,"score_spread":0.3124634538031414,"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."}}