{"id":"W4400188830","doi":"10.1016/j.media.2024.103257","title":"The ACROBAT 2022 challenge: Automatic registration of breast cancer tissue","year":2024,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"AI in cancer detection","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Orionin Tutkimussäätiö; Turun Yliopisto; Vetenskapsrådet; Cancerfonden; VINNOVA; Karolinska Institutet; European Commission; Swedish e-Science Research Centre; Turun Yliopistosäätiö; Syöpäsäätiö; European Federation of Pharmaceutical Industries and Associations","keywords":"Computer science; Artificial intelligence; Image registration; Breast cancer; Computer vision; Pattern recognition (psychology); Cancer; Medicine; Image (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":[0.008162214,0.002093426,0.001777729,0.002248797,0.00134676,0.003431415,0.002609147,0.002917008,0.004211222],"category_scores_gemma":[0.01718893,0.0008295417,0.002412809,0.002149035,0.001292206,0.001898306,0.004397436,0.002727568,0.006933951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008597063,"about_ca_system_score_gemma":0.002470666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004708676,"about_ca_topic_score_gemma":0.007574426,"domain_scores_codex":[0.9940713,0.001395048,0.0005221037,0.001939636,0.00166953,0.0004023159],"domain_scores_gemma":[0.9941163,0.001661335,0.0004862564,0.002427391,0.0009886899,0.0003199496],"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":[0.002960552,0.0008672832,0.01769871,0.003043703,0.001492933,0.0009543382,0.0006680366,0.03272856,0.1123201,0.007057912,0.3212436,0.4989643],"study_design_scores_gemma":[0.0009116941,0.001839285,0.05235955,0.0005549515,0.0005775582,0.009204036,0.001099537,0.3945322,0.1757356,0.02100463,0.3416368,0.0005440876],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2907127,0.01768125,0.5048829,0.006870252,0.006622998,0.002660964,0.07107416,0.07668898,0.02280576],"genre_scores_gemma":[0.3287923,0.002871411,0.4574893,0.002203457,0.0008328605,0.001491506,0.1822098,0.009695827,0.01441366],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008162214,"threshold_uncertainty_score":0.04316646,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00763111225579277,"score_gpt":0.3052339521346472,"score_spread":0.2976028398788544,"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."}}