Pham Huy Hieu, PhD.
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Dr. Pham Huy Hieu is an Assistant Professor at the College of Engineering and Computer Science (CECS), VinUniversity, and a Research Fellow cum Associate Director at VinUni-Illinois Smart Health Center. He received his Ph.D. in Computer Science from the Toulouse Computer Science Research Institute (IRIT), University of Toulouse, France, in 2019. Previously, he earned the Degree of Engineer in Industrial Informatics from Hanoi University of Science and Technology (HUST), Vietnam, in 2016. His research interests include Computer Vision, Machine Learning, Medical Image Analysis, and their applications in Smart Healthcare. He is the author, co-author of 45 scientific articles appeared in about 30 conferences and journals such as Nature Scientific Data, Computer Vision and Image Understanding, Neurocomputing, PloS ONE, Medical Physics, Frontiers in Digital Health, Biomedical Signal Processing and Control, International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Medical Imaging with Deep Learning (MIDL), IEEE International Conference on Image Processing (ICIP), and IEEE International Conference on Computer Vision (ICCV), Asian Conference on Computer Vision (ACCV). He is also currently serving as Reviewers for MICCAI, ICCV, CVPR, IEEE Journal of Biomedical and Health Informatics, and Nature Scientific Reports. Dr. Hieu Pham was recognized by the Federal Ministry of Education and Research (Germany) as an outstanding researcher in AI and Medical Imaging Research with DAAD Fellowship 2021. Recently, he received the AI Awards 2022 for the VAIPE project that he served as co-PI. He also co-authored the paper that won Best Paper Finalist Award in the 23rd International Symposium on Cluster, Cloud and Internet Computing (CCGrid 2023). Before joining VinUniversity, Dr. Hieu worked at Vingroup Big Data Institute (VinBigData) as a Research Scientist and Head of the Fundamental Research Team. With this position, he led several research projects on Medical AI, including collecting various types of medical data, managing and annotating data, and developing new AI solutions for medical analysis.
Recent Submissions
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Deployment and validation of an AI system for detecting abnormal chest radiographs in clinical settings
(2022-07-27)Background: The purpose of this paper is to demonstrate a mechanism for deploying and validating an AI-based system for detecting abnormalities on chest X-ray scans at the Phu Tho General Hospital, Vietnam. We aim to ... -
A novel multi-view deep learning approach for BI-RADS and density assessment of mammograms
(2022)Advanced deep learning (DL) algorithms may predict the patient’s risk of developing breast cancer based on the Breast Imaging Reporting and Data System (BI-RADS) and density standards. Recent studies have suggested that ... -
An Accurate and Explainable Deep Learning System Improves Interobserver Agreement in the Interpretation of Chest Radiograph
(2022-08-06)Interpretation of chest radiographs (CXR) is a difficult but essential task for detecting thoracic abnormalities. Recent artificial intelligence (AI) algorithms have achieved radiologist-level performance on various medical ... -
A Novel Transparency Strategy-based Data Augmentation Approach for BI-RADS Classification of Mammograms
(2022-03-20)Image augmentation techniques have been extensively studied to enhance the performance of deep learning (DL) algorithms in mammography classification tasks. Recent advancements have demonstrated the effectiveness of image ... -
Learning to diagnose common thorax diseases on chest radiographs from radiology reports in Vietnamese
(2022-10-07)Deep learning, in recent times, has made remarkable strides when it comes to impressive performance for many tasks, including medical image processing. One of the contributing factors to these advancements is the emergence ... -
Learning to Automatically Diagnose Multiple Diseases in Pediatric Chest Radiographs Using Deep Convolutional Neural Networks
(2021-10-17)Chest radiograph (CXR) interpretation is critical for the diagnosis of various thoracic diseases in pediatric patients. This task, however, is error-prone and requires a high level of understanding of radiologic expertise. ... -
VinDr-SpineXR: A deep learning framework for spinal lesions detection and classification from radiographs
(2021-06-24)Radiographs are used as the most important imaging tool for identifying spine anomalies in clinical practice. The evaluation of spinal bone lesions, however, is a challenging task for radiologists. This work aims at ...
