T
Tran Anh Tuan Data-verified
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
ARS
faculty
8 h-index
53 pubs
375 cited
Research Areas
Links
Biography and Research Information
OverviewAI-generated summary
Tran Anh Tuan's research focuses on the application of deep learning and machine learning techniques to medical imaging, particularly in the domain of neuroimaging and stroke prediction. His work investigates the development and improvement of models for segmentation and classification tasks, aiming to enhance their robustness and generalizability.
Recent publications by Tuan explore the use of deep learning for predicting hematoma expansion from CT scans in patients with intracerebral hemorrhage. He also investigates machine learning models for predicting 3-month outcomes in stroke patients based on radiomics from CT angiography. Tuan has published on segmenting white matter, gray matter, and cerebrospinal fluid from brain MRI using adaptive U-Net architectures.
His scholarship metrics include an h-index of 8, with 53 total publications and 368 citations. Tuan collaborates with researchers at the University of Arkansas at Fayetteville, including Sandra D. Ekşioğlu, Chase Rainwater, Jackson Cothren, and Enric Martí.
Metrics
- h-index: 8
- Publications: 53
- Citations: 375
Selected Publications
-
Land8Fire: A Complete Study on Wildfire Segmentation Through Comprehensive Review, Human-Annotated Multispectral Dataset, and Extensive Benchmarking (2025)
Collaboration Network
Top Collaborators
Seyedmehdi Payabvash
Columbia University Irving Medical Center (US)
16 shared publications
- Strategies to Improve the Robustness and Generalizability of Deep Learning Segmentation and Classification in Neuroimaging
- Uncertainty-aware deep-learning model for prediction of supratentorial hematoma expansion from admission non-contrast head computed tomography scan
- Deep learning for prediction of post-thrombectomy outcomes based on admission CT angiography in large vessel occlusion stroke
- Predicting hematoma expansion after intracerebral hemorrhage: a comparison of clinician prediction with deep learning radiomics models
- Peri-hematomal edema shape features related to 3-month outcome in acute supratentorial intracerebral hemorrhage
Showing 5 of 16 shared publications
Ajay Malhotra
Yale University (US)
12 shared publications
- Uncertainty-aware deep-learning model for prediction of supratentorial hematoma expansion from admission non-contrast head computed tomography scan
- Deep learning for prediction of post-thrombectomy outcomes based on admission CT angiography in large vessel occlusion stroke
- Peri-hematomal edema shape features related to 3-month outcome in acute supratentorial intracerebral hemorrhage
- Machine Learning Models for 3-Month Outcome Prediction Using Radiomics of Intracerebral Hemorrhage and Perihematomal Edema from Admission Head Computed Tomography (CT)
- Improving the Robustness of Deep Learning Models in Predicting Hematoma Expansion from Admission Head CT
Showing 5 of 12 shared publications
Tal Zeevi
Yale University (US)
11 shared publications
- Strategies to Improve the Robustness and Generalizability of Deep Learning Segmentation and Classification in Neuroimaging
- Uncertainty-aware deep-learning model for prediction of supratentorial hematoma expansion from admission non-contrast head computed tomography scan
- Deep learning for prediction of post-thrombectomy outcomes based on admission CT angiography in large vessel occlusion stroke
- Peri-hematomal edema shape features related to 3-month outcome in acute supratentorial intracerebral hemorrhage
- A Hybrid Transformer-Convolutional Neural Network for Segmentation of Intracerebral Hemorrhage and Perihematomal Edema on Non-Contrast Head Computed Tomography (CT) with Uncertainty Quantification to Improve Confidence
Showing 5 of 11 shared publications
Adnan I. Qureshi
Rehabilitation Institute of Kansas City (US)
11 shared publications
- Uncertainty-aware deep-learning model for prediction of supratentorial hematoma expansion from admission non-contrast head computed tomography scan
- Predicting hematoma expansion after intracerebral hemorrhage: a comparison of clinician prediction with deep learning radiomics models
- Peri-hematomal edema shape features related to 3-month outcome in acute supratentorial intracerebral hemorrhage
- Machine Learning Models for 3-Month Outcome Prediction Using Radiomics of Intracerebral Hemorrhage and Perihematomal Edema from Admission Head Computed Tomography (CT)
- Improving the Robustness of Deep Learning Models in Predicting Hematoma Expansion from Admission Head CT
Showing 5 of 11 shared publications
Guido J. Falcone
Yale University (US)
11 shared publications
- Uncertainty-aware deep-learning model for prediction of supratentorial hematoma expansion from admission non-contrast head computed tomography scan
- Deep learning for prediction of post-thrombectomy outcomes based on admission CT angiography in large vessel occlusion stroke
- Peri-hematomal edema shape features related to 3-month outcome in acute supratentorial intracerebral hemorrhage
- Machine Learning Models for 3-Month Outcome Prediction Using Radiomics of Intracerebral Hemorrhage and Perihematomal Edema from Admission Head Computed Tomography (CT)
- Improving the Robustness of Deep Learning Models in Predicting Hematoma Expansion from Admission Head CT
Showing 5 of 11 shared publications
Kevin N. Sheth
Yale University (US)
10 shared publications
- Uncertainty-aware deep-learning model for prediction of supratentorial hematoma expansion from admission non-contrast head computed tomography scan
- Peri-hematomal edema shape features related to 3-month outcome in acute supratentorial intracerebral hemorrhage
- Machine Learning Models for 3-Month Outcome Prediction Using Radiomics of Intracerebral Hemorrhage and Perihematomal Edema from Admission Head Computed Tomography (CT)
- Improving the Robustness of Deep Learning Models in Predicting Hematoma Expansion from Admission Head CT
- A Hybrid Transformer-Convolutional Neural Network for Segmentation of Intracerebral Hemorrhage and Perihematomal Edema on Non-Contrast Head Computed Tomography (CT) with Uncertainty Quantification to Improve Confidence
Showing 5 of 10 shared publications
Laurel R. Keyes
Salk Institute for Biological Studies (US)
8 shared publications
- Separable dorsal raphe dopamine projections mimic the facets of a loneliness-like state
- Social Exclusion Amplifies Behavioral Responses to Physical Pain via Insular Neuromodulation
- Separable Dorsal Raphe Dopamine Projections Mimic the Facets of a Loneliness-like State
- Separable Dorsal Raphe Dopamine Projections Mimic the Facets of a Loneliness-like State
- Separable Dorsal Raphe Dopamine Projections mediate the Facets of Loneliness-like state
Showing 5 of 8 shared publications
Caroline Jia
Howard Hughes Medical Institute (US)
8 shared publications
- Separable dorsal raphe dopamine projections mimic the facets of a loneliness-like state
- Social Exclusion Amplifies Behavioral Responses to Physical Pain via Insular Neuromodulation
- Separable Dorsal Raphe Dopamine Projections Mimic the Facets of a Loneliness-like State
- Separable Dorsal Raphe Dopamine Projections Mimic the Facets of a Loneliness-like State
- Separable Dorsal Raphe Dopamine Projections mediate the Facets of Loneliness-like state
Showing 5 of 8 shared publications
Faith Aloboudi
Salk Institute for Biological Studies (US)
8 shared publications
- Separable dorsal raphe dopamine projections mimic the facets of a loneliness-like state
- Social Exclusion Amplifies Behavioral Responses to Physical Pain via Insular Neuromodulation
- Separable Dorsal Raphe Dopamine Projections Mimic the Facets of a Loneliness-like State
- Separable Dorsal Raphe Dopamine Projections Mimic the Facets of a Loneliness-like State
- Separable Dorsal Raphe Dopamine Projections mediate the Facets of Loneliness-like state
Showing 5 of 8 shared publications
Romy Wichmann
Salk Institute for Biological Studies (US)
8 shared publications
- Separable dorsal raphe dopamine projections mimic the facets of a loneliness-like state
- Social Exclusion Amplifies Behavioral Responses to Physical Pain via Insular Neuromodulation
- Separable Dorsal Raphe Dopamine Projections Mimic the Facets of a Loneliness-like State
- Separable Dorsal Raphe Dopamine Projections Mimic the Facets of a Loneliness-like State
- Separable Dorsal Raphe Dopamine Projections mediate the Facets of Loneliness-like state
Showing 5 of 8 shared publications
Kay M. Tye
Massachusetts Institute of Technology (US)
8 shared publications
- Separable dorsal raphe dopamine projections mimic the facets of a loneliness-like state
- Social Exclusion Amplifies Behavioral Responses to Physical Pain via Insular Neuromodulation
- Separable Dorsal Raphe Dopamine Projections Mimic the Facets of a Loneliness-like State
- Separable Dorsal Raphe Dopamine Projections Mimic the Facets of a Loneliness-like State
- Separable Dorsal Raphe Dopamine Projections mediate the Facets of Loneliness-like state
Showing 5 of 8 shared publications
Christopher R. Lee
Howard Hughes Medical Institute (US)
7 shared publications
- Separable dorsal raphe dopamine projections mimic the facets of a loneliness-like state
- Social Exclusion Amplifies Behavioral Responses to Physical Pain via Insular Neuromodulation
- Separable Dorsal Raphe Dopamine Projections Mimic the Facets of a Loneliness-like State
- Separable Dorsal Raphe Dopamine Projections Mimic the Facets of a Loneliness-like State
- Separable Dorsal Raphe Dopamine Projections mediate the Facets of Loneliness-like state
Showing 5 of 7 shared publications
Pham The Bao
6 shared publications
- White Matter, Gray Matter and Cerebrospinal Fluid Segmentation from Brain Magnetic Resonance Imaging Using Adaptive U-Net and Local Convolutional Neural Network
- A survey of brain segmentation methods from magnetic resonance imaging
- Modeling Respiratory Signals by Deformable Image Registration on 4DCT Lung Images
- An Adaptive 3D U-Net for White Matter, Gray Matter and Cerebrospinal Fluid Segmentation from 3D-Brain MRI
- An effective approach for white matter, grey matter, and cerebrospinal fluid segmentation from 3D brain MRI
Showing 5 of 6 shared publications
Gaby Abou Karam
Yale University (US)
6 shared publications
- Uncertainty-aware deep-learning model for prediction of supratentorial hematoma expansion from admission non-contrast head computed tomography scan
- Improving the Robustness of Deep Learning Models in Predicting Hematoma Expansion from Admission Head CT
- A Hybrid Transformer-Convolutional Neural Network for Segmentation of Intracerebral Hemorrhage and Perihematomal Edema on Non-Contrast Head Computed Tomography (CT) with Uncertainty Quantification to Improve Confidence
- Optimizing Automated Hematoma Expansion Classification from Baseline and Follow-Up Head Computed Tomography
- Abstract TMP84: Multi-Institutional Validation of an Automated Segmentation Tool for Intracerebral Hemorrhage (ICH) and Peri-Hematomal Edema (PHE) on Head CT
Showing 5 of 6 shared publications
Gillian A. Matthews
Salk Institute for Biological Studies (US)
6 shared publications
- Separable dorsal raphe dopamine projections mimic the facets of a loneliness-like state
- Separable Dorsal Raphe Dopamine Projections Mimic the Facets of a Loneliness-like State
- Separable Dorsal Raphe Dopamine Projections Mimic the Facets of a Loneliness-like State
- Separable Dorsal Raphe Dopamine Projections mediate the Facets of Loneliness-like state
- Author response: Separable dorsal raphe dopamine projections mimic the facets of a loneliness-like state
Showing 5 of 6 shared publications
Similar Researchers
Based on overlapping research topics
Duy Lê
University of Arkansas at Fayetteville
Radiomics and Machine Learning in Medical Imaging
Advanced Neural Network Applications
Medical Imaging Techniques and Applications
M. Emre Celebi
University of Central Arkansas
Radiomics and Machine Learning in Medical Imaging
Advanced Neural Network Applications
Medical Imaging Techniques and Applications
William F. Griffin
University of Arkansas for Medical Sciences
Radiomics and Machine Learning in Medical Imaging
Advanced Neural Network Applications
Medical Imaging Techniques and Applications
Ting Liu
University of Arkansas at Fayetteville
Radiomics and Machine Learning in Medical Imaging
Advanced Neural Network Applications
Medical Imaging Techniques and Applications
Aditi Chaurasia
University of Arkansas for Medical Sciences
Radiomics and Machine Learning in Medical Imaging
Advanced Neural Network Applications
Medical Imaging Techniques and Applications
Eric Chang
Arkansas State University
Radiomics and Machine Learning in Medical Imaging
Advanced Neural Network Applications
Medical Imaging Techniques and Applications