{"url":"/dataset/luna","name":"LUNA","full_name":null,"description_markdown":"The **LUNA** challenges provide datasets for automatic nodule detection algorithms using the largest publicly available reference database of chest CT scans, the LIDC-IDRI data set. In [LUNA16](https://paperswithcode.com/dataset/luna16), participants develop their algorithm and upload their predictions on 888 CT scans in one of the two tracks: 1) the complete nodule detection track where a complete CAD system should be developed, or 2) the false positive reduction track where a provided set of nodule candidates should be classified.\r\n\r\nSource: [Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge](/paper/validation-comparison-and-combination-of)","description_withheld":null,"homepage":"https://luna16.grand-challenge.org/","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/validation-comparison-and-combination-of","title":"Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge","first_author":"Arnaud Arindra Adiyoso Setio","url":null},"license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Medical","url":"/datasets/modality/medical"}],"tasks":[{"name":"Lung Nodule Segmentation","url":"/task/lung-nodule-segmentation","datasets_with_task":"/datasets/task/lung-nodule-segmentation"},{"name":"Lung Nodule Detection","url":"/task/lung-nodule-detection","datasets_with_task":"/datasets/task/lung-nodule-detection"},{"name":"Computed Tomography (CT)","url":"/task/computed-tomography-ct","datasets_with_task":"/datasets/task/computed-tomography-ct"}],"languages":[],"variants":["LUNA","LUNA2016 FPRED","LUNA16"],"data_loaders":[{"repo":"https://github.com/BissenAbbassi/ComputerVision","url":"https://github.com/BissenAbbassi/ComputerVision","frameworks":["tf","pytorch"]}],"num_papers_in_archive":125,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/lung-nodule-segmentation-on-luna","task":"Lung Nodule Segmentation","dataset_variant":"LUNA","rows":5,"metrics":["AUC","F1 score","Accuracy","mIoU"],"first_row_in_archive_order":{"model":"BCDU-Net (d=3)","paper":"/paper/bi-directional-convlstm-u-net-with-densley","metrics":{"AUC":"0.9946","F1 score":"0.9904"},"code_links":[{"title":"rezazad68/BCDU-Net","url":"https://github.com/rezazad68/BCDU-Net"},{"title":"mmheydari97/BCDU-Net","url":"https://github.com/mmheydari97/BCDU-Net"},{"title":"CCChen19990820/Unet_Unetplusplus_BCDUnet_FRUnet","url":"https://github.com/CCChen19990820/Unet_Unetplusplus_BCDUnet_FRUnet"},{"title":"lyqcom/convlstm","url":"https://github.com/lyqcom/convlstm"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/lung-nodule-detection-on-luna2016-fpred","task":"Lung Nodule Detection","dataset_variant":"LUNA2016 FPRED","rows":2,"metrics":["AUC"],"first_row_in_archive_order":{"model":"Semantic Genesis","paper":"/paper/learning-semantics-enriched-representation","metrics":{"AUC":"98.47"},"code_links":[{"title":"JLiangLab/SemanticGenesis","url":"https://github.com/JLiangLab/SemanticGenesis"},{"title":"fhaghighi/SemanticGenesis","url":"https://github.com/fhaghighi/SemanticGenesis"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/learning-semantics-enriched-representation","title":"Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restoration","date":"2020-07-14","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/bi-directional-convlstm-u-net-with-densley","title":"Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions","date":"2019-08-31","rows_on_this_dataset":1,"code_links":4,"syntology":null},{"paper":"/paper/models-genesis-generic-autodidactic-models","title":"Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis","date":"2019-08-19","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/et-net-a-generic-edge-attention-guidance","title":"ET-Net: A Generic Edge-aTtention Guidance Network for Medical Image Segmentation","date":"2019-07-25","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/ce-net-context-encoder-network-for-2d-medical","title":"CE-Net: Context Encoder Network for 2D Medical Image Segmentation","date":"2019-03-07","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/road-extraction-by-deep-residual-u-net","title":"Road Extraction by Deep Residual U-Net","date":"2017-11-29","rows_on_this_dataset":1,"code_links":13,"syntology":null},{"paper":"/paper/u-net-convolutional-networks-for-biomedical","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","date":"2015-05-18","rows_on_this_dataset":1,"code_links":487,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":757,"samples_ran":518,"samples_unverified":239,"pointer_only_for_licence":426,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":1,"samples_harvested":757,"samples_ran":518,"samples_unverified":239,"pointer_only_for_licence":426,"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."}