4quant

The startup develops an image data analytics platform that leverages big data and deep learning to convert medical images into actionable information. This technology enables medical companies to enhance their data infrastructure by generating high-quality medical labels, addressing the critical need for reliable data in the medical AI sector.

Updated 2 months ago

Funding

Funding not disclosed

Funding rounds are not available yet.

Founders

Founder details are not available yet.

Product

Problem

Medical AI development is hindered by the lack of access to high-quality, systematically aggregated, and structured clinical data. Existing PACS and RIS systems are patient-focused, slow, difficult to search, and poorly scalable, making it challenging to extract meaningful insights from medical images.

Solution

4Quant provides a big image data analytics platform (BIDAP) that leverages big data and deep learning to transform medical images into actionable information. The platform integrates with existing PACS and RIS systems, providing high-speed, intelligent searches to create patient cohorts. Its HICCAP annotation platform enables continuous creation of curated data in the clinical setting, incorporating medical information into the workflow and scaling to thousands of annotators covering millions of patients. 4Quant's platform supports a range of modalities, including PET, CT, and MRI, and offers tools for quantitative analysis, statistical reporting, and integration with cloud computing infrastructure.

Target Audience

The primary target audience includes hospitals, clinics, and medical companies seeking to accelerate the development of AI-powered medical tools, gain clinical insights, and improve patient outcomes through advanced image analysis.

Features

HICCAP Annotation Platform for creating curated data with pixel-exact annotations and integration with existing systems
Big Image Data Analytics Platform (BIDAP) for applying complex operations across petabytes of data
Cloud Image Processing (CIP) framework for scaling ImageJ and FIJI tools in a distributed, fault-tolerant manner
4QL
Image Query Language for interactive image analysis using a simple query language
Integration with Apache Spark for scalable, fault-tolerant, distributed backend
Deep Learning integration with TensorFlow, Keras, and sklearn for object recognition, segmentation, and feature extraction
Support for various storage solutions, including Hadoop HDFS, Amazon S3, and OpenStack Swift
Volumetric analysis and quantification for precise characterization of disease