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ZENODO
Dataset . 2025
License: CC BY NC SA
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY NC SA
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY NC SA
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY NC SA
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY NC SA
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY NC SA
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY NC SA
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY NC SA
Data sources: Datacite
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The UNICORN Challenge: public few-shots

Authors: D'Amato, Marina; Weber, Rianne; Lefkes, Judith; van der Graaf, Fennie; Stegeman, Michelle; Grisi, Clément; Builtjes, Luc; +8 Authors

The UNICORN Challenge: public few-shots

Abstract

* Shared first authors: Clément Grisi, Michelle Stegeman, Judith Lefkes, Marina D'Amato, Rianne Weber, Luc Builtjes, Lena Philipp, Fennie van der Graaf, Joeran Bosma ** Shared last authors: Alessa Hering, Francesco Ciompi The UNICORN (Unified beNchmark for Imaging in COmputational pathology, Radiology and Natural language) challenge is an innovative benchmarking challenge that is part of the MICCAI 2025 Lighthouse Challenges. The goal of UNICORN is to address the lack of a comprehensive, public benchmark for evaluating the performance of multimodal foundation models in medical imaging. It provides a unified set of 20 tasks that span both vision and language in the fields of radiology and digital pathology. This dataset includes publicly available few-shots cases for the challenge tasks. These examples aim to provide participants with an understanding of the data structure for each of the 20 tasks and can be used for local development. 

Related Organizations
Keywords

foundation model, benchmarking, artificial intelligence, digital pathology, radiology

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average