Shaping the future of diagnostic imaging

The 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.

Request Demo

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.

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

Enhancing MRI Brain Tumor Segmentation with an Additional Classification Network

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).

VinDr-RibCXR: A Benchmark Dataset for Automatic Segmentation and Labeling of Individual Ribs on Chest X-rays

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

Classification of Chest X-ray Pathologies in Pediatric Patients Using Deep Convolutional Neural Networks

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

Automatic Classification of Human Body Parts from X-ray Images Using Deep Convolutional Neural Networks

Dung V. Do, Hieu H. Pham, Ha Q. Nguyen – arXiv preprint.

A Novel Method for Classification of Multi-phase Abdominal CT Scans Using Deep Learning and Random Sampling

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

VinDr-CXR: An open dataset of chest X-rays with radiologist’s annotations

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.

Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy

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.