{"id":"W1543260786","doi":"10.1007/978-3-540-69423-6_43","title":"Cross-Modal Interaction and Integration with Relevance Feedback for Medical Image Retrieval","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Relevance feedback; Image retrieval; Information retrieval; Automatic image annotation; Relevance (law); Modality (human–computer interaction); Visual Word; Set (abstract data type); Vector space model; XML; Modal; Matching (statistics); Similarity (geometry); Artificial intelligence; Image (mathematics); World Wide Web","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"],"consensus_categories":[],"category_scores_codex":[0.0009448529,0.0004008058,0.0003739034,0.0004091035,0.0002563533,0.0009466586,0.001370046,0.0003059335,0.00001364045],"category_scores_gemma":[0.0003075927,0.0003151476,0.00006925706,0.0004329336,0.001089186,0.001243855,0.0003864689,0.0007130368,0.000006992327],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002716642,"about_ca_system_score_gemma":0.0004566672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001922167,"about_ca_topic_score_gemma":0.00004110849,"domain_scores_codex":[0.9967628,0.00002455291,0.0005070788,0.001263673,0.001038682,0.000403238],"domain_scores_gemma":[0.9976383,0.0005977124,0.0003372501,0.0006896819,0.0005963442,0.0001406732],"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":[0.0001930672,0.00006910498,0.00004258311,0.0001133596,0.00001150614,0.00004363726,0.000208927,0.0001491043,0.004123874,0.02423216,0.00006948861,0.9707432],"study_design_scores_gemma":[0.001251365,0.001707301,0.0006852555,0.001367732,0.00001882257,0.0004054004,4.833396e-7,0.7147651,0.1485899,0.1249772,0.00488903,0.001342334],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001420292,0.0001578773,0.9958923,0.001295687,0.0005294299,0.0005520871,0.000007081712,0.0002261385,0.001197327],"genre_scores_gemma":[0.09467085,0.0001159401,0.9022806,0.0008280273,0.000614203,0.0000271796,0.00002854552,0.00005017586,0.001384421],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9694008,"threshold_uncertainty_score":0.9999301,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0123740344966594,"score_gpt":0.2814428028291956,"score_spread":0.2690687683325362,"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."}}