Build high-quality datasets with VinLab comprehensive labeling solutions
Flexible and secure data flow
GUI & CLI
Image and annotation import/export via GUI. Convenient CLI for image and annotation import
Integration with external storage: your data will securely stay on your cloud (AWS, GCP, Azure)
DICOM Annotation Studio
Label data faster and more efficient with powerful AI assistants that are trained specifically for medical imaging.
VinLab Studio is built with PACS-style interface that enable standard 2D annotation capabilities: Bounding boxes, Polygons and Tags.
Annotations on multiple frames are drawn and tracked as a whole instance. Annotator can edit a sequence of annotation by modifying only key frames.
Instance segmentation across axial, sagittal, and coronal planes for maximum precision. Interpolation to accelerate the tedious labeling progress.
Are you interested in?
100+ experienced radiologists who have collaborated with us in creating high-quality datasets of multiple imaging modalities.
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.
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.
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.
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