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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.

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

  • 会议论文

  • 发表日期：

    2026年

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  • Bogdan Ionescu,
  • Henning Müller,
  • Dan-Cristian Stanciu,
  • Ahmedkhan Radzhabov,
  • Alba García Seco de Herrera,
  • Alexandra-Georgiana Andrei,
  • Alexandra Băicoianu,
  • Ana Neacşu,
  • Andrea Storås,
  • Asma Ben Abacha,
  • Benjamin Bracke,
  • Lea Reinartz,
  • Benjamin Lecouteux,
  • Christoph M. Friedrich,
  • Cynthia Sabrina Schmidt,
  • Corneliu-Nicolae Florea,
  • Diandra Fabre,
  • Didier Schwab,
  • Dimitar Dimitrov,
  • Emmanuelle Esperança-Rodier,
  • Gabriel Constantin,
  • Hendrik Damm,
  • Henning Schäfer,
  • Ivan Koychev,
  • Josiane Mothe,
  • Liviu-Daniel Ştefan,
  • Maja J. Hjuler,
  • Mehmet Kurt,
  • Meliha Yetisgen,
  • Michael A. Riegler,
  • Mihai Dogariu,
  • Mihai Ivanovici,
  • Ming Shan Hee,
  • Mohammad El Sakka,
  • Momina Ahsan,
  • Obioma Pelka,
  • Pål Halvorsen,
  • Preslav Nakov,
  • Raphael Brüngel,
  • Sarfraz Ahmad,
  • Steven A. Hicks,
  • Sushant Gautam,
  • Tabea M. G. Pakull,
  • Bahadır Eryılmaz,
  • Vajira Thambawita,
  • Vassili Kovalev,
  • Wen-Wai Yim,
  • Yuri Prokopchuk,
  • Zhuohan Xie

摘要

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.