Resources¶

• UNICORN Challenge proposal: https://zenodo.org/records/13981073
• Public few-shots data: https://doi.org/10.5281/zenodo.14832502
• UNICORN baseline code: https://github.com/DIAGNijmegen/unicorn_baseline
• UNICORN baseline template code: https://github.com/DIAGNijmegen/unicorn_baseline_template
• UNICORN evaluation toolkit: https://github.com/DIAGNijmegen/unicorn_eval

The recording of the UNICORN kick-off webinar can be found here:

The table below shows the public datasets that can be used for pre-training the foundation models, described per task. The name of the original dataset, the license for its use, the original public link, and related publications are provided.
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*This dataset provides full reports as captions, while this task expects a short caption corresponding to the conclusion section of a pathology report.

References¶

[1] Christof A. Bertram, Marc Aubreville, Christian Marzahl, Andreas Maier, and Robert Klopfleisch. A large-scale dataset for mitotic figure assessment on whole slide images of canine cutaneous mast cell tumor. Scientific data, 6(1):274, 2019.¶

[2] Wouter Bulten, Kimmo Kartasalo, Po-Hsuan Cameron Chen, Peter Ström, Hans Pinckaers, Kunal Nagpal, Yuannan Cai, David F. Steiner, Hester Van Boven, Robert Vink, et al. Artificial intelligence for diagnosis and gleason grading of prostate cancer: the panda challenge. Nature medicine, 28(1):154–163, 2022.¶

[3] Qian Da, Xiaodi Huang, Zhongyu Li, Yanfei Zuo, Chenbin Zhang, Jingxin Liu, Wen Chen, Jiahui Li, Dou Xu, Zhiqiang Hu, et al. Digestpath: A benchmark dataset with challenge review for the pathological detection and segmentation of digestive system. Medical Image Analysis, 80:102485, 2022.¶

[4] M.J.J. de Grauw, E. Th. Scholten, E.J. Smit, M.J.C.M. Rutten, M. Prokop, B. van Ginneken, and A. Hering. The uls23 challenge: a baseline model and benchmark dataset for 3d universal lesion segmentation in computed tomography. arXiv preprint arXiv:2406.05231, 2024.¶

[5] Ward Hendrix, Nils Hendrix, Ernst T. Scholten, Mariëlle Mourits, Joline Trap-de Jong, Steven Schalekamp, Mike Korst, Maarten van Leuken, Bram van Ginneken, Mathias Prokop, et al. Deep learning for the detection of benign and malignant pulmonary nodules in non-screening chest ct scans. Communications Medicine, 3(1):156, 2023.¶

[6] Radboudumc. Dragon challenge. https://dragon.grand-challenge.org/.¶

[7] Radboudumc. Leopard challenge. https://leopard.grand-challenge.org/.¶

[8] Radboudumc. Spider challenge. https://spider.grand-challenge.org/.¶

[9] Radboudumc. Tiger challenge. https://tiger.grand-challenge.org/.¶

[10] Radboudumc. Uls’23 challenge. https://uls23.grand-challenge.org.¶

[11] Anindo Saha, Joeran S. Bosma, Jasper J. Twilt, Bram van Ginneken, Anders Bjartell, Anwar R. Padhani, David Bonekamp, Geert Villeirs, Georg Salomon, Gianluca Giannarini, et al. Artificial intelligence and radiologists in prostate cancer detection on mri (pi-cai): an international, paired, non-inferiority, confirmatory study. The Lancet Oncology, 2024.¶

[12] Jasper W. van der Graaf, Miranda L. van Hooff, Constantinus F.M. Buckens, Matthieu Rutten, Job L.C. van Susante, Robert Jan Kroeze, Marinus de Kleuver, Bram van Ginneken, and Nikolas Lessmann. Lumbar spine segmentation in mr images: a dataset and a public benchmark. Scientific Data, 11(1):264, 2024.¶

[13] Rami S. Vanguri, Jia Luo, Andrew T. Aukerman, Jacklynn V. Egger, Christopher J. Fong, Natally Horvat, Andrew Pagano, Jose de Arimateia Batista Araujo-Filho, Luke Geneslaw, Hira Rizvi, et al. Multimodal integration of radiology, pathology and genomics for prediction of response to pd-(l) 1 blockade in patients with non-small cell lung cancer. Nature cancer, 3(10):1151–1164, 2022.¶

[14] Kiran Vaidhya Venkadesh, Arnaud A.A. Setio, Anton Schreuder, Ernst T. Scholten, Kaman Chung, Mathilde M.W. Wille, Zaigham Saghir, Bram van Ginneken, Mathias Prokop, and Colin Jacobs. Deep learning for malignancy risk estimation of pulmonary nodules detected at low-dose screening ct. Radiology, 300(2):438–447, 2021.¶

[15] Pingyi Chen, Honglin Li, Chenglu Zhu and Sunyi Zheng. WsiCaption: multiple instance generation of pathology reports for gigapixel whole-slide images. International Conference on Medical Image Computing and Computer-Assisted Intervention, 15004:546-556, 2024.¶