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ImageCLEF 2026: Multimodal Challenges in Medicine, Science, Agritech, and Security

Ionescu, Bogdan
; 
Müller, Henning
; 
Stanciu, Dan-Cristian
; 
Radzhabov, Ahmedkhan
; 
de Herrera, Alba García Seco
; 
Andrei, Alexandra-Georgiana
; 
Băicoianu, Alexandra
; 
Neacşu, Ana
; 
Storås, Andrea
; 
Abacha, Asma Ben
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Author
Ionescu, Bogdan
Müller, Henning
Stanciu, Dan-Cristian
Radzhabov, Ahmedkhan
de Herrera, Alba García Seco
Andrei, Alexandra-Georgiana
Băicoianu, Alexandra
Neacşu, Ana
Storås, Andrea
Abacha, Asma Ben
Bracke, Benjamin
Reinartz, Lea
Lecouteux, Benjamin
Friedrich, Christoph M
Schmidt, Cynthia Sabrina
Florea, Corneliu-Nicolae
Fabre, Diandra
Schwab, Didier
Dimitrov, Dimitar
Esperança-Rodier, Emmanuelle
Constantin, Gabriel
Damm, Hendrik
Schäfer, Henning
Koychev, Ivan
Mothe, Josiane
Ştefan, Liviu-Daniel
Hjuler, Maja J
Kurt, Mehmet
Yetisgen, Meliha
Riegler, Michael A
Dogariu, Mihai
Ivanovici, Mihai
Hee, Ming Shan
El Sakka, Mohammad
Ahsan, Momina
Pelka, Obioma
Halvorsen, Pål
Nakov, Preslav
Brüngel, Raphael
Ahmad, Sarfraz
Hicks, Steven A
Gautam, Sushant
Pakull, Tabea MG
Eryılmaz, Bahadır
Thambawita, Vajira
Kovalev, Vassili
Yim, Wen-Wai
Prokopchuk, Yuri
Xie, Zhuohan
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Department
Natural Language Processing
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Conference proceeding
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Abstract
Since its inception in 2003, the various ImageCLEF challenges have provided large and complex datasets targeting a wide array of subjects in medicine, argumentation, reasoning, content recommendation, data generation, and question answering. In its 24th edition at CLEF, ImageCLEF will have five main tasks: (i) a Medical task, which aims to promote the synergy between four medical challenges: Caption, involving concept detection and caption prediction in radiology images, Synthetic Medical Image Generation in the GANs task, Visual Question Answering for improving the diagnosis and classification of real medical gastrointestinal images, and multimodal dermatology response generation and a new MEDIQA-CORE challenge focusing on predicting or correcting tumor type labels and identifying and summarizing major and minor differences between pairs of radiology reports; (ii) the ToPicto task, involving text to pictogram translation and prediction, (iii) the Multimodal Reasoning task on visual, multi-language, interdisciplinary question answering, and a task using multi-spectral remote sensing images: (iv) AI4Agriculture, involving predicting agricultural potential before planing and crop type identification, and (v) the Deepfake detection and generation task. In its last edition, 56 teams finished our challenges, continuing to show the impact in the community.
Citation
B. Ionescu, H. Müller, D.-C. Stanciu, A. Radzhabov, A.G.S. de Herrera, A.-G. Andrei , et al., "ImageCLEF 2026: Multimodal Challenges in Medicine, Science, Agritech, and Security," 2026, pp. 336-344.
Source
Lecture Notes in Computer Science
Conference
48th European Conference on Information Retrieval, ECIR 2026
Keywords
46 Information and Computing Sciences, 4602 Artificial Intelligence, 4605 Data Management and Data Science
Subjects
Source
48th European Conference on Information Retrieval, ECIR 2026
Publisher
Springer Nature
DOI
10.1007/978-3-032-21321-1_44
Additional links
https://link.springer.com/chapter/10.1007/978-3-032-21321-1_44
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