Build high-quality datasets with VinLab comprehensive labeling solutions
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Annotation types

Enhance 2D detection and labeling accuracy through the implementation of optimized bounding boxes.

Elevate the accuracy of AI models by utilizing polygon annotations for precise object identification.

Semantic segmentation associates every pixel of an image with a class label such as bones, internal organs, nodules and so on. It treats multiple objects of the same class as a single entity.

Instance segmentation treats multiple findings of the same class as distinct individual instances.

Our services

Data Annotation

Data Platform

Data Collection

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Data Annotation

Gather Expert Insight

Our annotation service boasts a scalable network of top-notch annotators, including a workforce of 100+ leading radiologists and doctors.

With our best-in-class annotation service, you can trust that your data will be accurately and efficiently annotated to meet your needs.

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Data Platform

For Medical

Accelerate and streamline the data labeling process for medical imaging with our powerful AI assistants and tools, built for maximum efficiency.

Our annotation platform also includes external storage integration and robust project management capabilities, ensuring that your data is managed and secured with the highest standards of reliability and accuracy.


Data Collection

Is The Key

Capture a wide range of medical modalities and pathologies with our comprehensive data collection service.

We collect diverse medical data from modalities such as X-ray, Mammography, CT, MRI, Ultrasound, Endoscopy, EEG, ECG, and many more, ensuring that your data needs are met with the utmost breadth and depth.


Our use cases


This is a large-scale dataset of chest X-ray images that was created via the VinDr Lab platform. It contains more than 18,000 CXR scans collected from two major hospitals in Vietnam. The images were labeled for the presence of 28 different radiographic findings and diagnoses in collaboration with a total of 17 experienced radiologists. VinDr-CXR is currently the largest dataset with radiologist-generated annotations. The dataset is explored to organize a competition hosted by the Kaggle platform.


Vingroup Big Data Institute (VinBigdata) has created and made freely available the VinDr-SpineXR: A large-scale X-ray dataset for spinal lesions detection and classification. The VinDr-SpineXR contains 10,469 images from 5,000 studies that are manually annotated with 13 types of abnormalities, each scan was annotated by an expert radiologist. To the best of our knowledge, the VinDr-SpineXR is currently the largest dataset to date that provides radiologist’s bounding-box annotations for developing supervised-learning object detection algorithms.


VinDr-RibCXR is a dataset for automatic segmentation and labeling of individual ribs from chest X-ray (CXR) scans. The VinDr-RibCXR contains 245 CXRs with corresponding ground truth annotations provided by human experts. Each image was assigned to an expert, who manually segmented and annotated each of 20 ribs, denoted as L1→L10 (left ribs) and R1→R10 (right ribs). The masks of ribs (see Figure 1) were then stored in a JSON file that can later be used for training instance segmentation models.

Our contributions

Open Datasets

Browse medical imaging open source datasets for your next machine learning projects. We hope that the public datasets will accelerate the speed of AI application in Healthcare.

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Open Source

Our community version (VinDr Lab) is available under an open-source, commercially-permissive software license (MIT). The license does not impose restrictions on the use of the software.

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Interested in building high-quality medical datasets?
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