TY  - JOUR
T1  - Overview of ImageCLEFMedical 2025 GANs Task: Training Data Analysis and Fingerprint Detection
A1  - Andrei, Alexandra-Georgiana
A1  - Constantin, Mihai Gabriel
A1  - Dogariu, Mihai
A1  - Stefan, Liviu-Daniel
A1  - Prokopchuk, Yuri
A1  - Kovalev, Vassili
A1  - Müller, Henning
A1  - Ionescu, Bogdan
JA  - CLEF 2025 Working Notes, 9 – 12 September 2025, Madrid, Spain / coeur-ws.org
Y1  - 2025
VL  - 4038
SP  - paper 169
UR  - https://ceur-ws.org/Vol-4038/paper_169.pdf
KW  - Deep Learning
KW  - Generative Adversarial Networks
KW  - generative models
KW  - ImageCLEF benchmarking lab
KW  - medical imaging
KW  - medical synthetic data
N2  - The 2025 ImageCLEFmedical GANs Task - Controlling the Quality of Synthetic Medical Images created via GANs,
continuing to investigate privacy and security concerns around using patient data to generate synthetic medical
images. It comprises two complementary sub-tasks: the first extends prior editions by asking participants to
detect which real images were used in training a Generative Adversarial Network to produce given synthetic
outputs; the second builds on the 2024 findings by requiring teams to attribute each synthetic image to its specific
real-image subset of origin. Ground-truth annotations and benchmark datasets of real and GAN-generated lung
CT slices are provided for both tasks, and evaluation is based on Cohen’s Kappa for Subtask 1 and accuracy for
Subtask 2. 14 teams submitted runs for Subtask1 and 4 teams submitted runs for Subtask 2, totaling 95 submitted
runs that used a variety of methods. This paper presents an overview of the task setup, datasets, and evaluation
metrics, and summarizes and discusses the approaches and results of the .
ER  -