{"id":"W4414629206","doi":"10.3390/technologies13100434","title":"Application of Foundation Models for Colorectal Cancer Tissue Classification in Mass Spectrometry Imaging","year":2025,"lang":"en","type":"article","venue":"Technologies","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kingston Health Sciences Centre; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Colorectal cancer; Foundation (evidence); Cancer; Tumor ablation; Cancer imaging; Scalability; Curse of dimensionality","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002185544,0.001056689,0.0006749331,0.000890175,0.0003744237,0.000817634,0.0008147524,0.000883112,0.0009056065],"category_scores_gemma":[0.003594964,0.0002900115,0.0009617263,0.0004864607,0.0003822449,0.0008767764,0.0007891806,0.001133243,0.0005296304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008343995,"about_ca_system_score_gemma":0.001395881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01162024,"about_ca_topic_score_gemma":0.01358108,"domain_scores_codex":[0.9994382,0.0001908614,0.00002963052,0.0001509764,0.0001090218,0.00008128435],"domain_scores_gemma":[0.9983369,0.000882887,0.0001520626,0.0001403987,0.000425416,0.00006223395],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004948667,0.0002291303,0.01168326,0.000111869,0.0002290034,0.0001646711,0.0001169693,0.6891863,0.01182627,0.003384852,0.004253203,0.2783195],"study_design_scores_gemma":[0.000004746275,0.00004088953,0.0006333333,0.00000728566,0.00001244002,0.00001941867,0.000009824264,0.9967949,0.001096372,0.0009909787,0.0003828542,0.000007015262],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2851731,0.001261604,0.7042328,0.0008323033,0.0001414827,0.0001476716,0.0008405444,0.004199511,0.003171024],"genre_scores_gemma":[0.8555838,0.0004603759,0.138533,0.0003064559,0.00007949411,0.0001440205,0.001695074,0.0001618623,0.00303607],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01162024,"threshold_uncertainty_score":0.0231052,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01201680126639037,"score_gpt":0.296849428427527,"score_spread":0.2848326271611366,"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."}}