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

Ionescu, Bogdan
; 
Muller, Henning
; 
Stanciu, Dan-Cristian
; 
Idrissi-Yaghir, Ahmad
; 
Radzhabov, Ahmedkhan
; 
Garcia Seco de Herrera, Alba
; 
Andrei, Alexandra
; 
Storas, Andrea
; 
Ben Abacha, Asma
; 
Bracke, Benjamin
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Author
Ionescu, Bogdan
Muller, Henning
Stanciu, Dan-Cristian
Idrissi-Yaghir, Ahmad
Radzhabov, Ahmedkhan
Garcia Seco de Herrera, Alba
Andrei, Alexandra
Storas, Andrea
Ben Abacha, Asma
Bracke, Benjamin
Lecouteux, Benjamin
Stein, Benno
Macaire, Cecile
Friedrich, Christoph M.
Schmidt, Cynthia Sabrina
Fabre, Diandra
Schwab, Didier
Dimitrov, Dimitar
Esperanca-Rodier, Emmanuelle
Constantin, Gabriel
Becker, Helmut
Damm, Hendrik
Schafer, Henning
Rodkin, Ivan
Koychev, Ivan
Kiesel, Johannes
Ruckert, Johannes
Malvehy, Josep
Stefan, Liviu-Daniel
Bloch, Louise
Potthast, Martin
Heinrich, Maximilian
Riegler, Michael A.
Dogariu, Mihai
Codella, Noel
Halvorsen, Pal
Nakov, Preslav
Brungel, Raphael
Novoa, Roberto Andres
Das, Rocktim Jyoti
Hicks, Steven A.
Gautam, Sushant
Pakull, Tabea M. G.
Thambawita, Vajira
Kovalev, Vassili
Yim, Wen-Wai
Xie, Zhuohan
Supervisor
Department
Natural Language Processing
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Conference proceeding
Date
2025
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Language
English
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Abstract
ImageCLEF has been a part of CLEF (Conference and Labs of the Evaluation Forum) for more than 20 years. Started in 2003, ImageCLEF is an evaluation initiative which promotes the evaluation of technologies for annotation, indexing, retrieval, or generation of multimodal data. It provides access to large amounts of challenging data in very diverse use cases like medicine, argumentation, reasoning, generation, or content recommendation. In its 23rd edition, ImageCLEF will have four main tasks: (i) a Medical task involving concept detection and caption prediction in radiology images, synthetic medical images created with Generative Adversarial Networks (GANs), Visual Question Answering for improving the diagnosis and classification of real medical gastrointestinal images, and multimodal dermatology response generation, (ii) a joint ImageCLEF-Touché task Image Retrieval/Generation for Arguments to convey the premise of an argument, (iii) the ToPicto task which involves converting either text or speech into a meaningful sequence of pictograms and (iv) a new Multimodal Reasoning task addressing question answering and reasoning generation. In its last edition in 2024, 90 users and 31 unique teams submitted runs, totaling 257 runs, revealing a good impact in the community, similar to previous years.
Citation
B. Ionescu et al., “ImageCLEF 2025: Multimedia Retrieval in Medical, Social Media and Content Recommendation Applications,” pp. 398–406, 2025, doi: 10.1007/978-3-031-88720-8_60.
Source
47th European Conference on Information Retrieval, ECIR 2025
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Publisher
Springer Nature
DOI
10.1007/978-3-031-88720-8_60
Additional links
https://link.springer.com/chapter/10.1007/978-3-031-88720-8_60
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