{ "schema_version": 1, "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/" }, "updated_at": "2026-09-13T00:00:00Z", "publication_count": 19, "publications": [ { "id": "spectral-adapters-sam-crlm-2026", "title": "Spectral Adapters for Segment Anything Model-based Segmentation of Colorectal Liver Metastases in Computed Tomography", "year": 2026, "publication_date": "2026-09-10", "group": "2026", "status": "Preprint · arXiv · Not peer reviewed", "authors": [ "Ramtin Mojtahedi", "Mohammad Hamghalam", "Jacob J. Peoples", "Natalie Gangai", "Mithat Gonen", "Yun Shin Chun", "HyunSeon Christine Kang", "Richard K. G. Do", "Amber L. Simpson" ], "authors_html": "R. Mojtahedi, M. Hamghalam, J. J. Peoples, N. Gangai, M. Gonen, Y. S. Chun, H. C. Kang, R. K. G. Do, and A. L. Simpson", "citation": "arXiv:2609.11703 [cs.CV] (2026). Version 1, submitted 10 September 2026.", "citation_plain": "arXiv:2609.11703 [cs.CV] (2026). Version 1, submitted 10 September 2026.", "doi": "10.48550/arXiv.2609.11703", "url": "https://arxiv.org/abs/2609.11703", "link_label": "arXiv ↗", "manual": true, "source": "Public arXiv record, verified 13 September 2026", "last_verified": "2026-09-13", "title_aliases": [], "venue": "arXiv", "volume": "", "issue": "", "pages": "", "article_number": "", "type": "preprint", "peer_reviewed": false, "publisher": "arXiv", "keywords": [ "colorectal liver metastases", "computed tomography", "Segment Anything Model", "spectral adapters", "DiSECT", "SiGA", "parameter-efficient fine-tuning", "medical image segmentation" ], "note": "Public preprint; not peer reviewed. 13 pages, 1 figure, 4 tables.", "detail_url": "/publications/spectral-adapters-sam-crlm-2026/", "open_access_url": "https://arxiv.org/pdf/2609.11703", "open_access_label": "Preprint PDF", "open_access_license": "", "preprint_url": "https://arxiv.org/abs/2609.11703", "code_url": "", "code_label": "", "pubmed_url": "", "pmc_url": "", "citation_pdf_url": "https://arxiv.org/pdf/2609.11703", "bibtex_type": "misc", "bibtex_key": "Mojtahedi2026SpectralAdapters", "eprint": "2609.11703", "primary_class": "cs.CV", "abstract": "Accurate segmentation of colorectal liver metastases (CRLM) in contrast-enhanced computed tomography (CT) is important for response assessment, surgical planning, and follow-up. We propose two parameter-efficient spectral adapters for the Segment Anything Model (SAM): the Directional Spectral Adapter (DiSECT) and Spectral Instance-Guided Adapter (SiGA). DiSECT uses singular value decomposition of frozen weights to constrain residual updates to leading spectral directions, while SiGA adds global and input-conditioned gating through a multilayer perceptron. We evaluate these methods on 446 contrast-enhanced CT volumes (355 training, 91 testing) and compare them with LoRA, QLoRA, convolutional adapters (CAD), and a 3D nnU-Net baseline. Experiments consider single-point, three-point, bounding-box, and no-prompt regimes. SiGA achieves the best single-point performance with a Dice score of 0.77, IoU of 0.69, and HD95 of 35.39 mm. Under no-prompt inference, SiGA reaches 0.76 Dice, 0.68 IoU, and 46.76 mm HD95, comparable to the nnU-Net baseline (0.758 Dice). DiSECT uses only 0.14 million trainable parameters. These results show that spectral adapters can efficiently adapt SAM for CRLM segmentation while retaining strong accuracy with limited trainable parameters." }, { "id": "multimodal-preoperative-risk-stratification-2026", "title": "Improving Preoperative Risk Stratification in Colorectal Liver Metastases: A Multi-Institutional Evaluation of Multimodal Prediction Models", "year": 2026, "publication_date": "2026-07-14", "group": "2026", "status": "Online ahead of print · Open access", "authors": [ "Kaitlyn S. M. Kobayashi", "Ramtin Mojtahedi", "E. Claire Bunker", "Joanne F. Chou", "Michael I. D’Angelica", "Natalie Gangai", "Mithat Gönen", "Mohammad Hamghalam", "Anish Jain", "HyunSeon Christine Kang", "Erin Kwak", "Jacob J. Peoples", "Alexandra H. Robins", "Katy L. Scott", "Richard K. G. Do", "Yun Shin Chun", "Amber L. Simpson" ], "authors_html": "K. S. M. Kobayashi*, R. Mojtahedi*, E. C. Bunker, J. F. Chou, M. I. D’Angelica, N. Gangai, M. Gönen, M. Hamghalam, A. Jain, H. C. Kang, E. Kwak, J. J. Peoples, A. H. Robins, K. L. Scott, R. K. G. Do, Y. S. Chun, and A. L. Simpson", "citation": "Scientific Reports (2026). Published 14 July 2026.", "citation_plain": "Scientific Reports (2026). Published 14 July 2026.", "doi": "10.1038/s41598-026-60860-9", "url": "https://doi.org/10.1038/s41598-026-60860-9", "link_label": "DOI ↗", "manual": true, "source": "Publisher and DOI metadata", "last_verified": "2026-08-02", "title_aliases": [], "venue": "Scientific Reports", "volume": "", "issue": "", "pages": "", "article_number": "", "type": "journal-article", "peer_reviewed": true, "publisher": "Springer Nature", "keywords": [ "colorectal liver metastases", "multimodal prediction", "radiomics", "clinical risk stratification" ], "note": "Nature currently provides an unedited early-access version; volume and article number have not yet been assigned.", "contribution_note": "Kaitlyn S. M. Kobayashi and Ramtin Mojtahedi contributed equally.", "detail_url": "/publications/multimodal-preoperative-risk-stratification-2026/", "open_access_url": "https://www.nature.com/articles/s41598-026-60860-9", "open_access_label": "Open article", "open_access_license": "CC BY-NC-ND 4.0", "preprint_url": "", "code_url": "https://github.com/kaitlyn-kobayashi/CRLM_Clinical_Radiomics_Analysis", "code_label": "Code", "pubmed_url": "https://pubmed.ncbi.nlm.nih.gov/42448749/", "pmc_url": "", "citation_pdf_url": "", "bibtex_type": "article", "bibtex_key": "Kobayashi2026PreoperativeRisk" }, { "id": "carotid-plaque-radiomics-risk-2026", "title": "Radiomic Carotid Plaque Features Integrated into Machine Learning Models for Cardiovascular Risk Prediction", "year": 2026, "publication_date": "2026-06-12", "group": "2026", "status": "In press · Corrected proof", "authors": [ "Ricky Hu", "Ramtin Mojtahedi", "Ergi Duli", "Marie-France Hétu", "Laura Mantella", "Amber L. Simpson", "Michael J. Blaha", "Taylor Barwell", "Kiera Liblik", "Jasjit S. Suri", "Amer M. Johri" ], "authors_html": "R. Hu*, R. Mojtahedi*, E. Duli, M.-F. Hétu, L. Mantella, A. L. Simpson, M. J. Blaha, T. Barwell, K. Liblik, J. S. Suri, and A. M. Johri", "citation": "Ultrasound in Medicine & Biology. Corrected proof online 12 June 2026. *Co-first authors.", "citation_plain": "Ultrasound in Medicine & Biology. Corrected proof online 12 June 2026.", "doi": "10.1016/j.ultrasmedbio.2026.05.006", "url": "https://doi.org/10.1016/j.ultrasmedbio.2026.05.006", "link_label": "DOI ↗", "manual": true, "source": "Publisher and DOI metadata; OpenAlex/ORCID", "last_verified": "2026-08-02", "title_aliases": [], "venue": "Ultrasound in Medicine & Biology", "volume": "", "issue": "", "pages": "", "article_number": "", "type": "journal-article", "peer_reviewed": true, "publisher": "Elsevier", "keywords": [ "carotid plaque", "radiomics", "cardiovascular risk", "machine learning" ], "note": "Issue, volume, and page range have not yet been assigned by the journal.", "contribution_note": "Ricky Hu and Ramtin Mojtahedi are co-first authors.", "detail_url": "/publications/carotid-plaque-radiomics-risk-2026/", "open_access_url": "", "open_access_label": "", "open_access_license": "", "preprint_url": "", "code_url": "https://github.com/Ramtin-Mojtahedi/AI-Radiomics-Carotid", "code_label": "Code", "pubmed_url": "https://pubmed.ncbi.nlm.nih.gov/42285860/", "pmc_url": "", "citation_pdf_url": "", "bibtex_type": "article", "bibtex_key": "Hu2026CarotidRadiomics" }, { "id": "foundation-models-liver-tumor-segmentation-2026", "title": "Parameter-Efficient Fine-Tuning of Foundation Models for Liver Tumor Segmentation in CT", "year": 2026, "publication_date": "2026-04-08", "group": "2026", "status": "Published", "authors": [ "Ramtin Mojtahedi", "Mohammad Hamghalam", "Jacob J. 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SPIE 13926, 1392612.", "citation_plain": "Medical Imaging 2026: Computer-Aided Diagnosis, Proceedings of SPIE 13926, 1392612.", "doi": "10.1117/12.3087835", "url": "https://doi.org/10.1117/12.3087835", "link_label": "DOI ↗", "manual": true, "source": "Publisher and DOI metadata; OpenAlex/ORCID; Crossref", "last_verified": "2026-08-02", "title_aliases": [], "venue": "Medical Imaging 2026: Computer-Aided Diagnosis", "volume": "13926", "issue": "", "pages": "260-268", "article_number": "1392612", "type": "conference-paper", "peer_reviewed": true, "publisher": "SPIE", "keywords": [ "foundation models", "parameter-efficient fine-tuning", "liver tumor segmentation", "computed tomography" ], "note": "", "detail_url": "/publications/foundation-models-liver-tumor-segmentation-2026/", "open_access_url": "", "open_access_label": "", "open_access_license": "", "preprint_url": "", "code_url": "https://github.com/Ramtin-Mojtahedi/peft-sam-liver-ct", "code_label": "Code", "pubmed_url": "", "pmc_url": "", "citation_pdf_url": "", "abstract": "State-of-the-art interactive segmentation with foundation models remains constrained by compute and the need for annotations. We evaluated parameter-efficient fine-tuning (PEFT) of the Segment Anything Model (SAM) by inserting baseline adapters, Low-Rank Adaptation (LoRA), 4-bit Quantized Low-Rank Adaptation (QLoRA), and a convolutional adapter (Conv-Adapter), as well as our proposed Directional Spectral Top-K adapter (DiSCo), training only the adapters while keeping the SAM backbone frozen. DiSCo performs singular value decomposition of row-normalized weights to obtain spectral bases, then learns rank-gated spectral coefficients, per-output magnitude offsets, and a spectral gain; it supports optional Top-K rank selection at inference while keeping only 0.14 M parameters trainable. We benchmarked five prompting regimes, none, single-point, multi-point, and bounding boxes at an intersection over union (IoU) of 0.50 and 0.75, applied to abdominal CT scans of colorectal liver metastases, and reported segmentation metrics (Dice and the 95th-percentile Hausdorff distance (HD95)) and compute metrics (trainable parameters, latency, memory, throughput). Conv-Adapter and LoRA achieved the highest accuracy (overall Dice 0.793 and 0.792; single-point 0.795 and 0.792; HD95 32 mm). QLoRA was close (overall 0.766; single-point 0.768; HD95 36.41 mm) while offering the most favorable compute profile (0.91 M trainable parameters, 120 ms latency, 4.9 GB peak memory). DiSCo maximized parameter efficiency, achieving the highest Dice per million trainable parameters (4.66), showing an accuracy-efficiency trade-off (overall Dice 0.653; single-point 0.698; HD95 49.53 mm). 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Listed as a conference presentation, not as a verified peer-reviewed paper.", "detail_url": "/publications/dilated-transformer-liver-cancers-2024/", "open_access_url": "", "open_access_label": "", "open_access_license": "", "preprint_url": "", "code_url": "", "code_label": "Code", "pubmed_url": "", "pmc_url": "", "citation_pdf_url": "", "bibtex_type": "misc", "bibtex_key": "Mojtahedi2024DilatedTransformer" }, { "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). 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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" }, { "id": "simsiam-liver-cancer-classification-2023", "title": "Leveraging Contrastive Learning with SimSiam for the Classification of Primary and Secondary Liver Cancers", "year": 2023, "publication_date": "2023", "group": "2022-24", "status": "Published", "authors": [ "Ramtin Mojtahedi", "Mohammad Hamghalam", "William R. Jarnagin", "Richard K. G. Do", "Amber L. Simpson" ], "authors_html": "R. Mojtahedi, M. Hamghalam, W. R. Jarnagin, R. K. G. Do, and A. L. Simpson", "citation": "Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops, Lecture Notes in Computer Science 14394, pp. 311–321 (2023). First online 3 February 2024.", "citation_plain": "Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops, Lecture Notes in Computer Science 14394, 311–321 (2023). First online 3 February 2024.", "doi": "10.1007/978-3-031-47425-5_28", "url": "https://doi.org/10.1007/978-3-031-47425-5_28", "link_label": "DOI ↗", "manual": true, "source": "Publisher and DOI metadata; OpenAlex/ORCID; Crossref", "last_verified": "2026-08-02", "title_aliases": [], "venue": "Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops", "volume": "14394", "issue": "", "pages": "311-321", "article_number": "", "type": "conference-paper", "peer_reviewed": true, "publisher": "Springer", "keywords": [ "contrastive learning", "SimSiam", "liver cancer classification", "self-supervised learning" ], "note": "Conference year is 2023; the Springer chapter was first published online in 2024.", "detail_url": "/publications/simsiam-liver-cancer-classification-2023/", "online_date": "2024-02-03", "open_access_url": "", "open_access_label": "", "open_access_license": "", "preprint_url": "", "code_url": "https://github.com/Ramtin-Mojtahedi/SimSiam-LiverCancer-CL", "code_label": "Code", "pubmed_url": "", "pmc_url": "", "citation_pdf_url": "", "abstract": "Accurate liver cancer classification is essential, as it substantially influences the selection of effective treatment strategies and impacts patient prognosis. Convolutional neural network (CNN) classifiers typically require extensive labeled datasets for training to attain decent performance. However, the process of obtaining labeled data through manual labeling is time-consuming, potentially biased, and costly when applied to large datasets. This study utilizes the Simple Siamese (SimSiam) contrastive self-supervised learning approach to enhance the classification of liver tumours, especially considering the limited availability of labeled computed tomography (CT) scans of liver cancer. We integrate SimSiam with three baseline CNN-based classifiers - Inception, Xception, and ResNet152 - and pretrain them with two loss functions: mean squared error (MSE) and cosine similarity (COS). Our findings show consistent improvements for three classifiers compared to the baseline models. Specifically, the ResNet152 model exhibits the highest performance among the evaluated networks. With MSE and COS losses, the classification accuracy for ResNet152 improves by 1.27% and 2.53%, respectively. The classification accuracy of the Inception model improves by 3.95% and 5.26%. Similarly, Xception's validation accuracy demonstrates an increase of 2.60% with both loss functions, compared to the baseline models. We validate our pipeline via our multi-resolution in-house abdominal CT scans of primary and secondary liver cancers, including 155 patients with hepatocellular carcinoma, 198 patients with colorectal liver metastases, and 107 patients with intrahepatic cholangiocarcinoma.", "bibtex_type": "inproceedings", "bibtex_key": "Mojtahedi2023SimSiam" }, { "id": "optimal-vit-patch-size-tumor-segmentation-2022", "title": "Towards Optimal Patch Size in Vision Transformers for Tumor Segmentation", "year": 2022, "publication_date": "2022-10-12", "group": "2022-24", "status": "Published · Preprint available", "authors": [ "Ramtin Mojtahedi", "Mohammad Hamghalam", "Richard K. G. 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