Sở Y tế Phú Thọ ký kết hợp tác xây dựng và triển khai giải pháp VinDr trong hỗ trợ chẩn đoán hình ảnh y tế
Trong những năm qua, tỉnh Phú Thọ đã đặc biệt trú trọng đến việc phát triển và ứng dụng...
Read MoreThe medical imaging team at Vingroup Big Data Institute (VinBigdata) conducts research in collecting, processing, analyzing, and understanding medical data. We aim to build large-scale and high-precision medical imaging solutions based on the latest advancements in artificial intelligence (AI) to facilitate effective clinical workflows.
VinDr-ChestXR is an AI-powered diagnosis system for chest X-ray interpretation. It is able to identify 6 lung diseases and localize 22 types of common abnormalities on chest X-ray.
The system has been trained and validated on half a million chest X-ray studies from both public sources and several hospitals in Vietnam. The bounding box annotation and disease labeling for our private dataset have been performed by top Vietnamese radiologists. The accuracy of the system is above 90% for almost all diseases and findings.
VinDr-Mammo is an AI-powered diagnosis system for mammography interpretation. It is able to classify a mammography study into 3 BI-RADS (Breast Imaging-Reporting and Data System) levels and 4 types of breast density. The system can also localize 13 types of common abnormalities on mammography.
VinDr-Mammo has been trained and validated on about 50,000 studies collected from several hospitals in Vietnam. The bounding box annotation and disease labeling for this private dataset have been performed by top Vietnamese radiologists. The accuracy for BI-RADS classification is above 80%.
VinDr-ChestCT is an AI-powered diagnosis system for Chest CT interpretation. It is able to identify 6 thoracic diseases and localize 24 types of common abnormalities on Chest CT scans.
The system will be trained and validated on about 30,000 studies collected from both public sources and several hospitals in Vietnam. The bounding box annotation (on 3-D volumes) and disease labeling for our private dataset have been performed by top Vietnamese radiologists.
VinDr-LiverCT is an AI-powered diagnosis system for abdomen CT interpretation. It is able to identify 10 liver diseases, including different types of liver cancer, and localize 24 types of common liver abnormalities on abdomen CT scans.
The system will be trained and validated on about 10,000 studies collected from both public sources and several hospitals in Vietnam. The bounding box annotation (on 3-D volumes) and disease labeling for our private dataset have been performed by top Vietnamese radiologists.
VinDr-BrainCT is an AI-powered diagnosis system for brain CT interpretation. It is able to identify 9 brain diseases, including several types of stroke, and localize 17 types of common abnormalities on brain CT scans.
The system will be trained and validated on about 30,000 studies collected from both public sources and several hospitals in Vietnam. The bounding box annotation (on 3-D volumes) and disease labeling for our private dataset have been performed by top Vietnamese radiologists.
VinDr-BrainMR is an AI-powered diagnosis system for brain MRI interpretation. It is able to identify 9 brain diseases, including brain tumor, and localize 20 types of common abnormalities on brain CT scans.
The system will be trained and validated on about 3,000 studies collected from both public sources and several hospitals in Vietnam. The bounding box annotation (on 3-D volumes) and disease labeling for our private dataset have been performed by top Vietnamese radiologists.
Hieu H. Pham, Tung T. Le, Dat Q. Tran, Dat T. Ngo, Ha Q. Nguyen – Full-length paper, Neurocomputing (IF: 4.434), to appear.
Hieu H. Pham, Tung T. Le, Dat Q. Tran, Dat T. Ngo, Ha Q. Nguyen – Short paper, Proceedings of Medical Imaging with Deep Learning (MIDL 2020).
Nhan T. Nguyen, Dat Q. Tran, Dung B. Nguyen – IEEE International Symposium on Biomedical Imaging (ISBI 2020).
Nhan T. Nguyen, Dat Q. Tran, Nghia T. Nguyen, Ha Q. Nguyen – Short paper, Proceedings of Medical Imaging with Deep Learning (MIDL 2020).
Huy Hieu Pham, Houssam Salmane, Louahdi Khoudour, Alain Crouzil, Sergio A. Velastin, and Pablo Zegers – Special Issue Camera as a Smart-Sensor (Volume 20, Issue 7), Intelligent Sensors 2020 (IF: 3.03).
Ph.D. UIUC, M.Sc. MIT, Postdoc EPFL
Department HeadB.Sc. UET
AI Research EngineerM.Sc. Coventry University
Software EngineerB.Sc. HAUI
Software EngineerB.Sc. FTU
Business AnalystHUST
AI Research InternUET
AI Research InternUOS
AI Research InternKAIST
AI Research InternTrong những năm qua, tỉnh Phú Thọ đã đặc biệt trú trọng đến việc phát triển và ứng dụng...
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