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Overview of ImageCLEF 2025: Multimedia Retrieval in Medical, Social Media and Content Recommendation Applications

Ionescu, Bogdan
; 
Müller, Henning
; 
Stanciu, Dan-Cristian
; 
Andrei, Alexandra-Georgiana
; 
Radzhabov, Ahmedkhan
; 
Prokopchuk, Yuri
; 
Ştefan, Liviu-Daniel
; 
Constantin, Mihai Gabriel
; 
Dogariu, Mihai
; 
Kovalev, Vassili
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Author
Ionescu, Bogdan
Müller, Henning
Stanciu, Dan-Cristian
Andrei, Alexandra-Georgiana
Radzhabov, Ahmedkhan
Prokopchuk, Yuri
Ştefan, Liviu-Daniel
Constantin, Mihai Gabriel
Dogariu, Mihai
Kovalev, Vassili
Damm, Hendrik
Rückert, Johannes
Abacha, Asma Ben
de Herrera, Alba G. Seco
Friedrich, Christoph M.
Bloch, Louise
Brüngel, Raphael
Idrissi-Yaghir, Ahmad
Schäfer, Henning
Schmidt, Cynthia Sabrina
Pakull, Tabea M. G.
Bracke, Benjamin
Pelka, Obioma
Eryılmaz, Bahadır
Becker, Helmut
Yim, Wen-Wai
Codella, Noel
Novoa, Roberto Andres
Malvehy, Josep
Dimitrov, Dimitar
Das, Rocktim Jyoti
Xie, Zhuohan
Hee, Ming Shan
Nakov, Preslav
Koychev, Ivan
Hicks, Steven A.
Gautam, Sushant
Riegler, Michael A.
Thambawita, Vajira
Halvorsen, Pål
Fabre, Diandra
Macaire, Cécile
Lecouteux, Benjamin
Schwab, Didier
Potthast, Martin
Heinrich, Maximilian
Kiesel, Johannes
Wolter, Moritz
Anand, Sharat
Stein, Benno
Supervisor
Department
Natural Language Processing
Embargo End Date
Type
Conference proceeding
Date
2025
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Language
English
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Abstract
This paper presents an overview of the ImageCLEF 2025 lab, which was organized within the Conference and Labs of the Evaluation Forum – CLEF Labs 2025. ImageCLEF is an ongoing evaluation event that started in 2003, promoting the evaluation of technologies for annotation, indexing, and retrieval of multimodal data and aiming to provide access to large collections of data across a veriety of scenarios, domains and contexts. In 2025, the 23rd edition of ImageCLEF consists of four main tasks: (i) the Medical task, comprised of four sub-tasks, approaching a wide array of problems in the medical field, like concept detection, caption prediction, explainability assessment in radiology images, evaluating the veracity of GAN-generated 3D CT scans, providing a segmentation and answers to close-ended questions regarding dermatology images, or visual question answering and synthetic image generation involving gastrointestinal images, (ii) a new Multimodal Reasoning task, involving answering multiple-choice questions in 13 different languages, covering a wide range of subjects and difficulty levels, (iii) the ToPicto task, which focuses on converting either text or speech into a meaningful sequence of pictograms and (iv) the Argument-Image task, which explores the augmentation of arguments using images, by either retrieval or synthetic generation. This edition of the ImageCLEF benchmark attracted 193 teams that registered to the different tasks, of which 56 finished the challenges. This resulted in 493 submitted runs and a total of 45 working note papers. Overall, this year’s edition has been very successful, with the biggest number of teams, submissions and working notes papers since 2019.
Citation
B. Ionescu et al., “Overview of ImageCLEF 2025: Multimedia Retrieval in Medical, Social Media and Content Recommendation Applications,” LNCS, vol. 16089, pp. 290–314, 2026, doi: 10.1007/978-3-032-04354-2_17
Source
Lecture Notes in Computer Science
Conference
Experimental IR Meets Multilinguality, Multimodality, and Interaction (CLEF 2025)
Keywords
Medical Image Processing, Medical Image Caption Analysis, Medical Concept Prediction, Visual Question Answering, Generative Adversarial Networks, Synthetic Data Generation, Image Segmentation, Pictogram Communication, Multilingual, Image Retrieval, ImageCLEF
Subjects
Source
Experimental IR Meets Multilinguality, Multimodality, and Interaction (CLEF 2025)
Publisher
Springer Nature
DOI
10.1007/978-3-032-04354-2_17
Additional links
https://link.springer.com/chapter/10.1007/978-3-032-04354-2_17
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