{"url":"/dataset/the-uls23-challenge-test-set","name":"The ULS23 Challenge Test Set","full_name":null,"description_markdown":"The ULS23 test set contains 725 lesions from 284 patients of the Radboudumc and JBZ hospitals in the Netherlands. It is intended to be used to measure the performance of 3D universal lesion segmentation models for Computed Tomography (CT). To prepare the data, radiological reports from both participating institutions where searched using NLP tools identifying patients with measurable target lesions, indicating that these lesions were clinically relevant. A random sample of patients was selected, 56.3% of which were male and with diverse scanner manufacturers. The lesions were annotated in 3D by expert radiologists with over 10 years of experience in reading oncological scans. ULS23 is an open benchmark, and we invite ongoing submissions to advance the development of future ULS models.","description_withheld":null,"homepage":"https://uls23.grand-challenge.org/evaluation/test-phase-leaderboard/leaderboard/","introduced_date":"2024-03-17","introduced_date_note":null,"introduced_by":{"paper":"/paper/the-uls23-challenge-a-baseline-model-and","title":"The ULS23 Challenge: a Baseline Model and Benchmark Dataset for 3D Universal Lesion Segmentation in Computed Tomography","first_author":"M. J. J. de Grauw","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"3D","url":"/datasets/modality/3d"},{"name":"Medical","url":"/datasets/modality/medical"}],"tasks":[{"name":"Segmentation","url":"/task/segmentation","datasets_with_task":"/datasets/task/segmentation"},{"name":"Tumor Segmentation","url":"/task/tumor-segmentation","datasets_with_task":"/datasets/task/tumor-segmentation"}],"languages":[],"variants":["The ULS23 Challenge Test Set"],"data_loaders":[],"num_papers_in_archive":2,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/tumor-segmentation-on-the-uls23-challenge","task":"Tumor Segmentation","dataset_variant":"The ULS23 Challenge Test Set","rows":2,"metrics":["Average Dice","ChallengeScore","Long-Axis SMAPE","Short-Axis SMAPE"],"first_row_in_archive_order":{"model":"U-Mamba","paper":"/paper/u-mamba-enhancing-long-range-dependency-for","metrics":{"Average Dice":"0.708 ± 0.235","ChallengeScore":"0.735","Long-Axis SMAPE":"0.103","Short-Axis SMAPE":"0.118"},"code_links":[{"title":"oliverrensu/mvg","url":"https://github.com/oliverrensu/mvg"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/the-uls23-challenge-a-baseline-model-and","title":"The ULS23 Challenge: a Baseline Model and Benchmark Dataset for 3D Universal Lesion Segmentation in Computed Tomography","date":"2024-06-07","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/u-mamba-enhancing-long-range-dependency-for","title":"U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation","date":"2024-01-09","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}