News archive

Below is an overview of all news posts of the project

CHAIMELEON featured at ECR 2025: A Landmark Moment for Healthcare Data Reproducibility

March 3, 2025

CHAIMELEON to present cutting-edge AI solutions at ECR2025

January 9, 2025

Acknowledging our partners in a key milestone of the CHAIMELEON Project: The In-Silico Validation

January 8, 2025

Registrations available for Webinar Series on European Health Data Space Regulation

November 25, 2024

Registration open for the ESR-EIBIR Webinar on Advancements in Cancer Imaging

November 19, 2024

Save the date for the upcoming ESR-EIBIR Webinar on Advancements in Cancer Imaging

November 12, 2024

Extension of the Re-Identification Challenge

September 1, 2024

Project Highlights and Promotion of Work Packages

July 22, 2024

The “Re-Identification Challenge”: Now OPEN for registrations

May 3, 2024

CHAIMELEON M44 Consortium Meeting

April 10, 2024

CHAIMELEON at ECR 2024

February 12, 2024

Register now: CHAIMELEON Open Challenges

July 3, 2023

CHAIMELON Repository Expansion: Call for Collaborators

March 31, 2023

CHAIMELEON Open Challenges to open in May 2023

March 30, 2023

CHAIMELEON at ECR 2023

March 1, 2023

Project Newsletter Out

October 20, 2022

CHAIMELEON M25 Consortium Meeting

September 22, 2022

September 20, 2022

CHAIMELEON Video Interview

September 1, 2022

CHAIMELEON at ECR 2022

July 6, 2022

CHAIMELEON demo videos

June 27, 2022

CHAIMELEON project video

April 1, 2022

CHAIMELEON completes 1st project period

February 28, 2022

CHAIMELEON M12 Consortium Meeting

September 30, 2021

First Project Newsletter Out

September 30, 2021

First project year complete

September 28, 2021

CHAIMELEON M6 Consortium Meeting

March 2, 2021

Follow us on Twitter

December 1, 2020

CHAIMELEON kick-off meeting

September 4, 2020

CHAIMELEON Project launched

September 1, 2020

CHAIMELEON is a four-year research project developing an EU-wide repository for health-related imaging data as an open source for AI experimentation in cancer management.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 952172.

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