{"id":"W4313503391","doi":"10.3389/fradi.2022.1061402","title":"Relevance maps: A weakly supervised segmentation method for 3D brain tumours in MRIs","year":2022,"lang":"en","type":"article","venue":"Frontiers in Radiology","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; Hospital for Sick Children; University of Toronto","funders":"","keywords":"Segmentation; Computer science; Artificial intelligence; Convolutional neural network; Pattern recognition (psychology); Pipeline (software); Relevance (law); Similarity (geometry); Sørensen–Dice coefficient; Image segmentation; 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":[0.001216603,0.001384352,0.0008795392,0.001647987,0.0004980557,0.0008721228,0.001669007,0.001689287,0.001411128],"category_scores_gemma":[0.003479911,0.0008134212,0.001238842,0.0007858561,0.000959815,0.001065025,0.001507262,0.001357257,0.00120825],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007140414,"about_ca_system_score_gemma":0.001107654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002962479,"about_ca_topic_score_gemma":0.004507137,"domain_scores_codex":[0.9992909,0.000148512,0.0000334582,0.0002147653,0.0002440303,0.00006820012],"domain_scores_gemma":[0.9991635,0.0002679025,0.0001402983,0.0001354648,0.0002321689,0.00006070198],"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":[0.0006769164,0.0001631889,0.002377843,0.0002790975,0.0001562734,0.0004522453,0.0004526503,0.3522492,0.1197129,0.006606789,0.008300888,0.5085721],"study_design_scores_gemma":[0.00001366032,0.00005222108,0.000502606,0.000009940444,0.00001531571,0.0001274718,0.00001795327,0.9774652,0.01532961,0.005050329,0.001398318,0.0000174773],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01332676,0.0001761829,0.9831733,0.0001235679,0.00002827162,0.00008174627,0.00009851271,0.002478675,0.0005131135],"genre_scores_gemma":[0.3209948,0.0003558783,0.6722,0.0003018784,0.0001401054,0.0002598856,0.001015397,0.001208851,0.003523354],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002962479,"threshold_uncertainty_score":0.006434083,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02627921253878829,"score_gpt":0.2936797042652475,"score_spread":0.2674004917264592,"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."}}