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.

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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

Interpreting chest X-rays via CNNs that exploit disease dependencies and uncertainty labels

Hieu H. Pham, Tung T. Le, Dat Q. Tran, Dat T. Ngo, Ha Q. Nguyen – Full-length paper, Neurocomputing (IF: 4.434), to appear.

Interpreting chest X-rays via CNNs that exploit disease dependencies and uncertainty labels

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).

Detection and segmentation of endoscopic artefacts and disease using deep architectures

Nhan T. Nguyen, Dat Q. Tran, Dung B. Nguyen – IEEE International Symposium on Biomedical Imaging (ISBI 2020).

A CNN-LSTM Architecture for Detection of Intracranial Hemorrhage on CT scans

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

A Unified Deep Framework for Joint 3D Pose Estimation and Action Recognition from a Single RGB Camera

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).