{"id":"W2980081640","doi":"10.1007/978-3-030-32239-7_17","title":"Task Adaptive Metric Space for Medium-Shot Medical Image Classification","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Metric (unit); Artificial intelligence; Baseline (sea); Task (project management); Machine learning; Shot (pellet); Domain (mathematical analysis); Meta learning (computer science); Image (mathematics); Space (punctuation); Data mining; Pattern recognition (psychology); 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":[0.001103204,0.0008167999,0.001607527,0.0009388662,0.0003265902,0.000877958,0.00190048,0.001080372,0.002318495],"category_scores_gemma":[0.002706694,0.0003102518,0.0008971817,0.001361961,0.0004786781,0.00116028,0.001644133,0.00160166,0.001189046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006447241,"about_ca_system_score_gemma":0.000838708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003940077,"about_ca_topic_score_gemma":0.003529655,"domain_scores_codex":[0.9993327,0.0002265714,0.0000404368,0.0001463299,0.0001920019,0.00006187717],"domain_scores_gemma":[0.9991513,0.0003589327,0.00004807889,0.0001517585,0.0002295576,0.00006033159],"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":[0.0001857119,0.0001547769,0.0004432151,0.000206353,0.0001037788,0.00005315719,0.00007317448,0.10582,0.01474623,0.008860808,0.01088783,0.858465],"study_design_scores_gemma":[0.000004865031,0.00007547517,0.0004100025,0.00001161954,0.00001126264,0.00008267103,0.00002119344,0.9845389,0.003097619,0.009783968,0.001948463,0.00001401686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006280764,0.0008507773,0.9913078,0.0001232491,0.00005198072,0.00003882446,0.000167505,0.0006349038,0.0005441865],"genre_scores_gemma":[0.3174295,0.002008948,0.6674268,0.0003310489,0.0002711338,0.0003322969,0.00272297,0.000449653,0.009027763],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003940077,"threshold_uncertainty_score":0.007834256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03696009152504456,"score_gpt":0.284568985770106,"score_spread":0.2476088942450614,"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."}}