Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
Experience robust data engineering processes free from the challenges of infrastructure management. By utilizing straightforward, modular Python, you can define intricate streaming, scheduling, and data backfill pipelines with ease. Transition from traditional ETL methods and access your data instantly, regardless of its complexity. Seamlessly blend deep learning and large language models with structured business datasets to enhance decision-making. Improve forecasting accuracy using up-to-date information, eliminate the costs associated with vendor data pre-fetching, and conduct timely queries for online predictions. Test your ideas in Jupyter notebooks before moving them to a live environment. Avoid discrepancies between training and serving data while developing new workflows in mere milliseconds. Monitor all of your data operations in real-time to effortlessly track usage and maintain data integrity. Have full visibility into everything you've processed and the ability to replay data as needed. Easily integrate with existing tools and deploy on your infrastructure, while setting and enforcing withdrawal limits with tailored hold periods. With such capabilities, you can not only enhance productivity but also ensure streamlined operations across your data ecosystem.
Description
The Open Health Imaging Foundation (OHIF) Viewer is an open-source web platform dedicated to medical imaging, providing a robust framework for the creation of intricate imaging applications. It is designed to quickly load large radiology studies by pre-fetching essential metadata and streaming imaging pixel data as needed. With the integration of Cornerstone3D, it efficiently decodes, renders, and annotates medical images. Users benefit from seamless compatibility with DICOMWeb-compliant image archives and a data source API that allows for integration with proprietary API formats. The viewer’s plugin architecture enables the development of specialized workflow modes that make use of existing core functionalities. Additionally, its user interface, crafted using React.js and Tailwind CSS, not only boasts a visually appealing design but is also built for extensibility, featuring a library of reusable UI components that enhance overall usability and customization. This combination of features positions the OHIF Viewer as a versatile tool in the field of medical imaging.
API Access
Has API
Yes
API Access
Has API
Yes
Integrations
Amazon Redshift
Yes
Amazon S3
Yes
Amazon Web Services (AWS)
Yes
Azure Databricks
Yes
Datadog
Yes
Docker
Yes
GitHub
Yes
Google Cloud BigQuery
Yes
GraphQL
Yes
Melio
Yes
Integrations
Amazon Redshift
No
Amazon S3
No
Amazon Web Services (AWS)
No
Azure Databricks
No
Datadog
No
Docker
No
GitHub
No
Google Cloud BigQuery
No
GraphQL
No
Melio
No
Pricing Details
Free
Free Trial
No
Free Version
Yes
Pricing Details
Free
Free Trial
No
Free Version
Yes
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
Yes
Linux
Yes
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
Yes
In Person
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
Yes
In Person
No
Vendor Details
Company Name
Chalk
Country
United States
Website
www.chalk.ai/
Vendor Details
Company Name
OHIF
Country
United States
Website
docs.ohif.org
Product Features
Machine Learning
Deep Learning
No
ML Algorithm Library
No
Model Training
No
Natural Language Processing (NLP)
No
Predictive Modeling
No
Statistical / Mathematical Tools
No
Templates
No
Visualization
No