{"id":"W2966434031","doi":"10.1016/j.media.2020.101851","title":"Boundary loss for highly unbalanced segmentation","year":2020,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":459,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Segmentation; Boundary (topology); Computer science; Artificial intelligence; Computer vision; Mathematical optimization; Mathematics; Mathematical analysis","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001845682,0.00008511109,0.0001776375,0.0000893734,0.0001533093,0.00008418899,0.0001959331,0.00005206124,0.001069214],"category_scores_gemma":[0.002026368,0.00007577014,0.0001820529,0.001283885,0.0001541366,0.0001593131,0.00002375368,0.0001146349,0.0001402979],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002978056,"about_ca_system_score_gemma":0.00005015378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005631757,"about_ca_topic_score_gemma":0.000005962127,"domain_scores_codex":[0.9986355,0.00009643711,0.0002402218,0.0003736031,0.0004939159,0.0001602989],"domain_scores_gemma":[0.9992869,0.0001896466,0.00009219052,0.0001428219,0.00004686193,0.0002415609],"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":[0.00009967343,0.0001037518,0.0006206273,0.00004122585,0.0001260033,0.00004259856,0.0004250746,0.00002999958,0.9554171,0.0005354736,0.006527483,0.03603102],"study_design_scores_gemma":[0.001384337,0.0001349299,0.003609169,0.00000579981,0.0006087003,0.000007164625,0.0002351517,0.2961838,0.671142,0.0004462423,0.0259622,0.0002804757],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1143528,0.00001929646,0.8339949,0.04989741,0.0001469142,0.0002434999,0.00003460337,0.0002335828,0.001077042],"genre_scores_gemma":[0.9854594,0.0000233868,0.0008978408,0.01302909,0.0001634491,0.00005289902,0.00004072272,0.000009844254,0.0003233088],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8711067,"threshold_uncertainty_score":0.999844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03034175931016476,"score_gpt":0.3062704956848611,"score_spread":0.2759287363746963,"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."}}