Medical Imaging Data

Catalog Designer

Al-Accelerated

Tag automatically your medical images with al models trained on the largest european medical database with specific labels and consolidate your own medical imaging data catalog ready for al training.

DICOM exams categorization

Workflow scalable with high volume of data

End-to-end orchestration

Starting with MRI
Cardiovascular Images

Label provides AI models that automatically classify cardiac MRI images into the following categories: Format, Orientation, Exam Type, and Organ.

Format

Defines the image dimensionality

(2D+t, 3D, etc.)

Orientation

Refers to the standard cardiac imaging planes

(Axial, Sagittal, etc.)

Exam

Corresponds to the standard cardiac MRI acquisition

(Function, PC, etc.)

Organ

Identifies the Ascending & Descending aorta from the specific MRI acquisitions

(Function – 2D+t – Axial)

Deployed In Europe’s Largest Medical Data Warehouse

EDS/APHP uses label to turbocharge its AI-development capabilities

See Press Release

What Label Does

Label allows data owners to seamlessly structure and prepare their large imaging datasets for AI-Training

Create Structured Database

Extract, store and index DICOM data at a large scale with quality verification

Catalogue Your Database

Automatically recognize the images' format, orientation, exam type and imaged organ

Design Al Algorithm

Use your data catalog as source for AI Training

Where Label Can Help?

Label provide the foundation for imaging data owner to design their own catalog and to boost al expert's capabilities to build personalized & predictive al models from large imaging dataset.

Design Medical Imaging Data Catalog

Label provides al models that automatically classifies/labels imaging data based on sequences type, orientation, format and organ (with maximal abstraction of dicom meta-data).

Build Al Model From Large Imaging Datasets

label provides AI infrastruture for data management at large scale and adapted to training ml on medical imaging data.

Medical Images

+ 0

Key Features

Label empowers healthcare institutions to unlock the full potential of multi-dimensional cardiovascular MRI data and accelerate AI development

Data Pipeline

Seamlessly extract, normalize, validate and transform dicom data

Dedicated ML Models

Automatic classification data with new metadata in 3 categories: format, orientation and sequence type

Al Infrastruture

Capabilities to develop, evaluate, validate, deploy and monitor machine learning model

Large Data Management

Process large quantities of data runnable on several types of cluster

Scalable and Modular

EDA and versatile architecture fledged CI/CD and powered by kubenetes

On-Premise & Cloud

Deployable on-premise or on cloud services catering to your specific technical needs

You Are
a Medical Data Provider?

You Are
a Research Team?

Featured In

We’ve established Strategic Partnership with Medical Imaging Data Providers

Use case .01

Create a Data Cohort from a 12M Patient Database

Europe's largest medical database (EDS/APHP) works with Label to categorize large medical imaging datasets for AI training by using deep learning techniques that automatically recognize images' format, orientation, exam type, and imaged organ.

Use case .02

Design Cardiovascular MRI Data Catalog

One of the world's top 10 best hospitals (ICT/Pitié-Salpêtrière) works with label to design their cardiovascular MRI data catalog by Using a dedicated ML model that provides predictions on MRI data based on: sequence type, orientation, organ and format.

Use case .03

Manage Large Medical Imaging Databases

Novartis empower their Data 42 Program by leveraging label's expertise in managing large medical imaging databases : implement a large-scale data pipeline to manage DICOM data, including: extraction normalization, validation and transformation.

Latest From Label

May 29, 2025

Label By Imageens

Medical Imaging Data

Catalog Designer

Al-Accelerated