{"id":"W4415731737","doi":"10.1038/s43856-026-01558-x","title":"Interpretable Multiple Instance Learning for Hematologic Diagnosis from Peripheral Blood Smears","year":2025,"lang":"en","type":"preprint","venue":"Communications Medicine","topic":"Digital Imaging for Blood Diseases","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Cancer Institute; National Institutes of Health; Memorial Sloan-Kettering Cancer Center","keywords":"Interpretability; Encoder; Pattern recognition (psychology); Feature (linguistics); Function (biology); Pipeline (software); Softmax function; Peripheral blood","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":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.0002707442,0.0003472141,0.0006436824,0.0002196116,0.0003375695,0.0002528235,0.006651014,0.000150768,0.00002330155],"category_scores_gemma":[0.00283057,0.0003336085,0.0001891735,0.0003779104,0.0004330852,0.0003709131,0.006435159,0.0007338867,0.000006907643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001024064,"about_ca_system_score_gemma":0.0002640753,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004888719,"about_ca_topic_score_gemma":0.0001159216,"domain_scores_codex":[0.9979404,0.0001824439,0.0005949734,0.0007001209,0.000250882,0.0003311505],"domain_scores_gemma":[0.9921571,0.002281959,0.0003602856,0.004733326,0.0003223303,0.0001450098],"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":[0.000162256,0.008270615,0.3145592,0.002919721,0.004015174,0.0000631285,0.02515848,0.01099137,0.000626398,0.1443649,0.122598,0.3662707],"study_design_scores_gemma":[0.004200173,0.0003201154,0.003432771,0.009373729,0.0009575044,0.00001231989,0.0007815491,0.688072,0.000633421,0.09206349,0.1987107,0.001442172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01841715,0.1066771,0.8011754,0.03293818,0.002219841,0.002790394,0.0005020279,0.002201944,0.03307791],"genre_scores_gemma":[0.8328308,0.002070418,0.1602889,0.001192671,0.0000700091,0.001788736,0.0007345588,0.00002359822,0.001000272],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8144137,"threshold_uncertainty_score":0.9999116,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04791972255597907,"score_gpt":0.3228623583760989,"score_spread":0.2749426358201198,"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."}}