{"id":"W3193532867","doi":"10.1016/j.compmedimag.2021.101974","title":"An unsupervised method for histological image segmentation based on tissue cluster level graph cut","year":2021,"lang":"en","type":"article","venue":"Computerized Medical Imaging and Graphics","topic":"AI in cancer detection","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"National Cancer Institute; Fundamental Research Funds for the Central Universities; Natural Science Foundation of Shaanxi Province; National Natural Science Foundation of China; U.S. Department of Defense","keywords":"Jaccard index; Artificial intelligence; Computer science; Pattern recognition (psychology); Segmentation; Voronoi diagram; Cut; Cluster analysis; Image segmentation; Adjacency list; Graph partition; Graph; Computer vision; 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":[0.0005659777,0.0007616505,0.001054039,0.003122274,0.0008078318,0.001141111,0.001815208,0.001354238,0.00204674],"category_scores_gemma":[0.001381555,0.0007312598,0.001388985,0.002369811,0.0008623797,0.000827747,0.001013135,0.001469207,0.001236564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00074041,"about_ca_system_score_gemma":0.002123053,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006351245,"about_ca_topic_score_gemma":0.01367304,"domain_scores_codex":[0.9990915,0.0001153581,0.00004218308,0.0002163543,0.0004716361,0.00006300979],"domain_scores_gemma":[0.9989785,0.0002763681,0.00009369464,0.0001805402,0.0004184711,0.00005246143],"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":[0.0001605203,0.0001658946,0.001575126,0.0003092173,0.0002236182,0.0001787495,0.0002327769,0.05672615,0.136032,0.01220674,0.007586358,0.7846028],"study_design_scores_gemma":[0.00003928402,0.0001326386,0.003468862,0.00003699085,0.0001137833,0.0007716121,0.00009098313,0.9151991,0.05405388,0.01457075,0.01143339,0.00008868881],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003495952,0.00007273664,0.9948078,0.00006047997,0.00002747992,0.00006623485,0.00007410083,0.001074794,0.0003203796],"genre_scores_gemma":[0.02854838,0.0001042386,0.9691241,0.00006318936,0.0000278455,0.0001221565,0.0003349374,0.0003588216,0.001316328],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006351245,"threshold_uncertainty_score":0.01262856,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03159187053559112,"score_gpt":0.3381906320323873,"score_spread":0.3065987614967961,"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."}}