{"doi":"10.1364/boe.563694","title":"Leveraging pretrained vision transformers for automated cancer diagnosis in optical coherence tomography images","abstract":"This study presents an approach to brain cancer detection based on optical coherence tomography (OCT) images and advanced machine learning techniques. The research addresses the critical need for accurate, real-time differentiation between cancerous and noncancerous brain tissue during neurosurgical procedures. The proposed method combines a pre-trained large vision transformer (ViT) model, specifically DINOv2, with a convolutional neural network (CNN) operating on the grey level co-occurrence matrix (GLCM) texture features. This dual-path architecture leverages both the global contextual feature extraction capabilities of transformers and the local texture analysis strengths of GLCM + CNNs. To mitigate patient-specific bias from the limited cohort, we incorporate an adversarial discriminator network that attempts to identify individual patients from feature representations, creating a competing objective that forces the model to learn generalizable cancer-indicative features rather than patient-specific characteristics. We also explore an alternative state space model approach using MambaVision blocks, which achieves comparable performance. The dataset comprised OCT images from 11 patients, with 5,831 B-frame slices from 7 patients used for training and validation, and 1,610 slices from 4 patients used for testing. The model achieved high accuracy in distinguishing cancerous from noncancerous tissue, with over 99% accuracy on the training dataset, 98.8% on the validation dataset and 98.6% accuracy on the test dataset. This approach demonstrates significant potential for achieving and improving intraoperative decision-making in brain cancer surgeries, offering real-time, high-accuracy tissue classification and surgical guidance.","journal":"Biomedical Optics Express","year":2025,"id":553280,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9614,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":573580,"name":"Cheng-Yu Lee","orcid":"0000-0003-2291-1297","position":1,"is_corresponding":false},{"id":394110,"name":"Hyeon‐Cheol Park","orcid":null,"position":2,"is_corresponding":false},{"id":391306,"name":"David W. Nauen","orcid":"0000-0002-2652-9944","position":3,"is_corresponding":false},{"id":24634,"name":"Chetan Bettegowda","orcid":"0000-0001-9991-7123","position":4,"is_corresponding":false},{"id":295612,"name":"Xingde Li","orcid":"0000-0002-4725-3297","position":5,"is_corresponding":false},{"id":19509,"name":"Rama Chellappa","orcid":"0000-0002-7638-1650","position":6,"is_corresponding":false},{"id":1450318,"name":"Soumyajit Ray","orcid":null,"position":0,"is_corresponding":true}],"reference_count":24,"raw_metadata":null,"created_at":"2026-07-19T02:54:41.819682Z","pmid":"40809960","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}