Products

VinDr is a comprehensive solution for medical image analysis that integrates Artificial Intelligence (AI) into a Picture Archiving and Communication System (PACS) to assist radiologists in making fast and precise diagnoses. The system is able to store, manage, and communicate DICOM images; automatically localize abnormalities and suggest diagnosis in a real-time fashion. Focusing on some of the most common imaging modalities and highly-demanding diseases, VinDr offers 6 following modules.

VinDr-ChestXR

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

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

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

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

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

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. 

Research

We are passionate about applying computer vision (CV), machine learning (ML) and deep learning (DL) models to build computed-aided detection (CAD) and computer aided diagnosis (CADx) systems from very large-scale clinical datasets of multiple imaging modalities (X-ray, CT, MRI, etc). Our research includes new methods and ML/DL models for radiologist-level understanding and interpretation from medical images. We aim to validate and publish our work on top-tier journals and conferences.

Medical Imaging

Hieu T. Nguyen, Tung T. Le, Thang V. Nguyen and Nhan T. Nguyen – The 6th International Workshop on Brain Lesions 2020, MICCAI 2020, Peru, October (2020).

Hoang C. Nguyen, Tung T. Le, Hieu H. Pham, Ha Q. Nguyen – arXiv preprint.

Thanh T. Tran, Hieu T. Nguyen, Hieu H. Pham, Ha Q. Nguyen – arXiv preprint.

Binh T. Dao, Thang V. Nguyen, Hieu H. Pham, Ha Q. Nguyen – arXiv preprint.

Ha Q. Nguyen, Khanh Lam, Linh T. Le, Hieu H. Pham, Dat Q. Tran, Dung B. Nguyen, Dung D. Le, Chi M. Pham, Hang T. T. Tong, Diep H. Dinh, Cuong D. Do, Luu T. Doan, Cuong N. Nguyen, Binh T. Nguyen, Que V. Nguyen, Au D. Hoang, Hien N. Phan, Anh T. Nguyen, Phuong H. Ho, Dat T. Ngo, Nghia T. Nguyen, Nhan T. Nguyen, Minh Dao, Van Vu – arXiv preprint.

Sharib Ali, Mariia Dmitrieva, Noha Ghatwary, Sophia Bano, Gorkem Polat, Alptekin Temizel, Adrian Krenzer, Amar Hekalo, Yun Bo Guo, Bogdan Matuszewski, Mourad Gridach, Irina Voiculescu, Vishnusai Yoganand, Arnav Chavan, Aryan Raj, Nhan T. Nguyen, Dat Q. Tran, Le Duy Huynh, Nicolas Boutry, Shahadate Rezvy, Haijian Chen, Yoon Ho Choi, Anand Subramanian, Velmurugan Balasubramanian, Xiaohong W. Gao, Hongyu Hu, Yusheng Liao, Danail Stoyanov, Christian Daul, Stefano Realdon, Renato Cannizzaro, Dominique Lamarque, Terry Tran-Nguyen, Adam Bailey, Barbara Braden, James East, Jens Rittscher – Medical Image Analysis Volume 70, May 2021.

Ngoc Huy Nguyen, Ha Quy Nguyen, Nghia Trung Nguyen, Thang Viet Nguyen, Hieu Huy Pham, Tuan Ngoc-Minh Nguyen – arXiv preprint.

Hieu H. Pham, Tung T. Le, Dat Q. Tran, Dat T. Ngo, Ha Q. Nguyen – Neurocomputing (IF: 4.434), Volume 437, 21 May 2021, Pages 186-194.

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).

Computer Vision

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).

Our Team

Ha Nguyen

Ph.D. UIUC, M.Sc. MIT, Postdoc EPFL

Department Head

Hieu Pham

Ph.D. University of Toulouse

Research Scientist

Nhan Nguyen

M.Sc. U of Cambridge, Kaggle Master

AI Research Engineer

Dung Nguyen

B.Sc. HUST, Kaggle Master

AI Research Engineer

Dat Ngo

B.Sc. UET, Kaggle Master

AI Research Engineer

Nghia Nguyen

B.Sc. UET

AI Research Engineer

Thang Nguyen

B.Sc. UET

AI Research Engineer

Long Dam

M.Sc. Coventry University

Software Engineer

Trung Nguyen

B.Sc. PTIT

AI Research Engineer

Dan Vu

B.Sc. FTU

Business Analyst

Phuc Truong

B.Sc. LQDTU

Software Engineer

Hieu Pham

B.Sc. HUST

Software Engineer

Hieu Nguyen

B.Sc. HUST

AI Research Engineer

Tung Le

B.Sc. UET

AI Research Engineer

Toan Nguyen

B.Sc. FPT University

Account manager

Tu Vu

B.Sc. HUST

Software Engineer

Manh Tran

B.Sc. NUS

Software Engineer

Binh Dao

UOS

AI Research Intern

News

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Contact Us

Medical Imaging Department – VinBigdata
Address: 9th floor, Century Tower, Times City, 458 Minh Khai, Hai Ba Trung, Ha Noi
Email: vindr.contact@vinbigdata.org