If you are a developer, data scientist or anyone who is interested in tech products, you might know about an array of open-source’s benefits. It helps us “stand on the shoulders of the giants”, make use of quality resources and shorten our project time.
In medical fields, the application of AI in diagnosis is increasingly expanding and developing, since then medical data labeling platforms are highly prioritized for development.
Let’s discover the Top 4 Free Open Source Medical Data Labeling Platforms which are a part of an AI product.
What is open-source software?
Open-source software has openly accessible source code that anyone may review, edit, and improve.
Open-source software is a source code its authors make available to others who want to view that code, copy it, learn from it, edit it, or share it.
Top 4 Open Sources Medical Data Labeling Platforms
VinLab
VinLab is an open-source platform for medical image annotation.
- Language: Python, Go, React
- Data types: Image (DICOM)
Features
- Manage full medical data cycle at they study level
- Track project progress and status of each task
- Create and share label groups
- Organize labels by order and hierarchy
- Annotation tools: Bounding box, Polygon, Brush, Notes, Comments
- Control versions of exported labels
Enterprise features
- Annotation in MPR view
- AI integration and other features
Pros
- Support DICOM image
- High-level web interface equipped with advanced annotation tools and project management features.
- No installation of an application needed to label
CVAT
Computer Vision Annotation Tool (CVAT) is a free and open source, interactive online tool for annotating videos and images for Computer Vision algorithms. It is used by tens of thousands of users and companies around the world.
- Language: Typescript, React, CSS, Python, mustache
- Data Types: Image, video
Feature
- Automatic annotation
- Can be used for Interpolation bounding boxes and attributes between multiple keyframes
- Attribute annotation mode and segmentation mode
- Annotation import and export
Pros
- Support DICOM data and multiple annotation formats
- Rapidly develop, deploy, and operate computer vision applications.
- No installation of an application needed to annotate
- Include a variety of annotation shapes: rectangles, polygons and polylines to points, cuboids, tags and tracks
- Supports multiple annotation formats such as CVAT, Pascal, XML, MS COCO, YOLO and TFRecords
- Supports interpolation
Source: https://github.com/opencv/cvat
Labelme
LabelMe is a free online annotation tool created by the MIT Computer Science and Artificial Intelligence Laboratory.
- Language: JavaScript
- Data types: Text, Image, audio, video
Features
- Image annotation for polygon, rectangle, circle, line and point.
- Image flag annotation for classification and cleaning.
- Video annotation.
- GUI customization (predefined labels/flags, auto-saving, label validation, etc).
- Exporting VOC-format dataset for semantic/instance segmentation. (semantic segmentation, instance segmentation)
- Exporting COCO-format dataset for instance segmentation. (instance segmentation)
Pros
- Supported diverse tools including bounding boxes, circles, ellipses, as well as polygons, points and polylines
- Support output formats: bounding boxes, circles, ellipses, as well as polygons, points and polylines
Labellmg
Labellmg is a free, open-source software program for labeling images using graphs. The software was released by Tzutalin in 2015 and is written in Python
- Data types: images, text, hypertext, audio, video, time-series data.
- Language: Python
Pros
- A straightforward and basic tool to label a few hundred images to create a dataset for computer vision model training
- Support diversity annotation format: PASCAL VOC, YOLO, Create ML
- Easy to install and run on a local PC
Thanks for reading!
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Contact
Email: info@vinlab.io
Twitter: https://twitter.com/VinLab_io
YouTube: https://www.youtube.com/@Vinlab-MedicalImageAnnotation
Open source project: https://github.com/vinbigdata-medical/vindr-lab
