{ "schema_version": 1, "record_url": "https://ramtin-mojtahedi.github.io/publications/multimodal-brain-tumour-vit-patch-2022/", "last_verified": "2026-08-02", "profile": { "name": "Ramtin Mojtahedi", "orcid": "https://orcid.org/0000-0002-3953-3256", "google_scholar": "https://scholar.google.com/citations?user=KjUrlGUAAAAJ&hl=en", "website": "https://ramtin-mojtahedi.github.io/" }, "publication": { "id": "multimodal-brain-tumour-vit-patch-2022", "title": "Multi-modal Brain Tumour Segmentation Using Transformer with Optimal Patch Size", "year": 2023, "publication_date": "2023-07-18", "group": "2022-24", "status": "Published", "authors": [ "Ramtin Mojtahedi", "Mohammad Hamghalam", "Amber L. Simpson" ], "authors_html": "R. Mojtahedi, M. Hamghalam, and A. L. Simpson", "citation": "Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Lecture Notes in Computer Science 13769, pp. 195–204 (2023). BrainLes 2022; first online 18 July 2023.", "citation_plain": "Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Lecture Notes in Computer Science 13769, 195–204 (2023). BrainLes 2022; first online 18 July 2023.", "doi": "10.1007/978-3-031-33842-7_17", "url": "https://doi.org/10.1007/978-3-031-33842-7_17", "link_label": "DOI ↗", "manual": true, "source": "Publisher and DOI metadata; OpenAlex/ORCID; Crossref", "last_verified": "2026-08-02", "title_aliases": [ "Towards an Optimal Patch Resolution in Vision Transformer for Multimodal Brain Tumour Segmentation" ], "venue": "Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (BrainLes 2022)", "volume": "13769", "issue": "", "pages": "195-204", "article_number": "", "type": "conference-paper", "peer_reviewed": true, "publisher": "Springer", "keywords": [ "brain tumor segmentation", "vision transformers", "patch size", "multimodal MRI" ], "note": "The linked repository is currently a bibliographic record; it should not be interpreted as a complete code release.", "detail_url": "/publications/multimodal-brain-tumour-vit-patch-2022/", "open_access_url": "", "open_access_label": "", "open_access_license": "", "preprint_url": "", "code_url": "https://github.com/Ramtin-Mojtahedi/BRATS_OVTPS", "code_label": "Repository record", "pubmed_url": "", "pmc_url": "", "citation_pdf_url": "", "abstract": "Early diagnosis and grading of gliomas are crucial for determining therapy and prognosis. For this purpose, magnetic resonance (MR) studies of brain tumours are commonly used. Due to the overlap between the intensity distributions of healthy, enhanced, non-enhancing, and edematous areas, automated segmentation is difficult. Convolutional neural networks (CNNs) have been utilized as the dominant networks for segmentation tasks. However, they suffer from the inability to capture long-range dependencies and global features due to their limited kernels. Vision transformers (ViTs) were introduced recently to tackle these limitations. Although transformers are capable of capturing long-range features, their segmentation performance falls as the variety of tumor sizes increases. In this paper, we propose a framework to find the ViT optimal patch resolution for the brain tumor segmentation task, particularly for segmenting smaller tumors. We validated our proposed framework on the BRaTS'21 dataset. This research lays the groundwork for future research on the semantic segmentation of tumor segmentation and detection using vision transformer-based networks for optimal outcomes.", "bibtex_type": "inproceedings", "bibtex_key": "Mojtahedi2022BrainTumour" }, "citation": { "plain_text": "Ramtin Mojtahedi, Mohammad Hamghalam, and Amber L. Simpson. “Multi-modal Brain Tumour Segmentation Using Transformer with Optimal Patch Size.” Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Lecture Notes in Computer Science 13769, 195–204 (2023). BrainLes 2022; first online 18 July 2023. https://doi.org/10.1007/978-3-031-33842-7_17", "bibtex": "@inproceedings{Mojtahedi2022BrainTumour,\n author = {Ramtin Mojtahedi and Mohammad Hamghalam and Amber L. Simpson},\n title = {{Multi-modal Brain Tumour Segmentation Using Transformer with Optimal Patch Size}},\n year = {2023},\n booktitle = {Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (BrainLes 2022)},\n volume = {13769},\n pages = {195-204},\n publisher = {Springer},\n doi = {10.1007/978-3-031-33842-7\\_17},\n url = {https://doi.org/10.1007/978-3-031-33842-7\\_17},\n note = {The linked repository is currently a bibliographic record; it should not be interpreted as a complete code release.}\n}", "ris": "TY - CPAPER\nTI - Multi-modal Brain Tumour Segmentation Using Transformer with Optimal Patch Size\nAU - Ramtin Mojtahedi\nAU - Mohammad Hamghalam\nAU - Amber L. Simpson\nPY - 2023\nDA - 2023-07-18\nT2 - Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (BrainLes 2022)\nVL - 13769\nSP - 195\nEP - 204\nDO - 10.1007/978-3-031-33842-7_17\nUR - https://doi.org/10.1007/978-3-031-33842-7_17\nKW - brain tumor segmentation\nKW - vision transformers\nKW - patch size\nKW - multimodal MRI\nN1 - The linked repository is currently a bibliographic record; it should not be interpreted as a complete code release.\nER -\n" }, "related_records": [ { "title": "Towards Optimal Patch Size in Vision Transformers for Tumor Segmentation", "year": 2022, "url": "https://ramtin-mojtahedi.github.io/publications/optimal-vit-patch-size-tumor-segmentation-2022/" }, { "title": "Self-Supervised Transformer-Based Pipeline for Liver Tumor Segmentation and Type Classification", "year": 2026, "url": "https://ramtin-mojtahedi.github.io/publications/self-supervised-transformer-liver-tumor-2026/" }, { "title": "Leveraging Contrastive Learning with SimSiam for the Classification of Primary and Secondary Liver Cancers", "year": 2023, "url": "https://ramtin-mojtahedi.github.io/publications/simsiam-liver-cancer-classification-2023/" } ] }