{"url":"/dataset/cell","name":"Cell","full_name":null,"description_markdown":"The CELL benchmark is made of fluorescence microscopy images of cell. \r\n\r\nSource: [Multi-Domain Adversarial Learning](/paper/multi-domain-adversarial-learning-1)\r\n\r\nImage Source: [https://arxiv.org/pdf/1903.09239v1.pdf](https://arxiv.org/pdf/1903.09239v1.pdf)","description_withheld":null,"homepage":"https://github.com/AltschulerWu-Lab/MuLANN","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/multi-domain-adversarial-learning-1","title":"Multi-Domain Adversarial Learning","first_author":"Alice Schoenauer-Sebag","url":null},"license":null,"modalities":[],"tasks":[{"name":"Medical Image Segmentation","url":"/task/medical-image-segmentation","datasets_with_task":"/datasets/task/medical-image-segmentation"},{"name":"Color Image Denoising","url":"/task/color-image-denoising","datasets_with_task":"/datasets/task/color-image-denoising"},{"name":"Nuclear Segmentation","url":"/task/nuclear-segmentation","datasets_with_task":"/datasets/task/nuclear-segmentation"}],"languages":[],"variants":["Cell17","Cell","CellNet"],"data_loaders":[{"repo":"https://github.com/AltschulerWu-Lab/MuLANN","url":"https://github.com/AltschulerWu-Lab/MuLANN","frameworks":["pytorch"]}],"num_papers_in_archive":12,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/nuclear-segmentation-on-cell17","task":"Nuclear Segmentation","dataset_variant":"Cell17","rows":4,"metrics":["Hausdorff","F1-score","Dice"],"first_row_in_archive_order":{"model":"Cell R-CNN","paper":"/paper/panoptic-segmentation-with-an-end-to-end-cell","metrics":{"Dice":"0.7088","F1-score":"0.8216","Hausdorff":"11.3141"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/color-image-denoising-on-cellnet","task":"Color Image Denoising","dataset_variant":"CellNet","rows":1,"metrics":["PSNR"],"first_row_in_archive_order":{"model":"DnCNN (n2t)","paper":"/paper/noise2self-blind-denoising-by-self","metrics":{"PSNR":"34.4"},"code_links":[{"title":"czbiohub/noise2self","url":"https://github.com/czbiohub/noise2self"},{"title":"mozanunal/SparseCT","url":"https://github.com/mozanunal/SparseCT"},{"title":"royerlab/ssi-code","url":"https://github.com/royerlab/ssi-code"},{"title":"abbasi-ali/noise2self","url":"https://github.com/abbasi-ali/noise2self"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/medical-image-segmentation-on-cell","task":"Medical Image Segmentation","dataset_variant":"Cell","rows":1,"metrics":["IoU"],"first_row_in_archive_order":{"model":"UNet++","paper":"/paper/unet-redesigning-skip-connections-to-exploit","metrics":{"IoU":"91.21"},"code_links":[{"title":"MrGiovanni/Nested-UNet","url":"https://github.com/MrGiovanni/Nested-UNet"},{"title":"MrGiovanni/UNetPlusPlus","url":"https://github.com/MrGiovanni/UNetPlusPlus"},{"title":"frgfm/Holocron","url":"https://github.com/frgfm/Holocron"},{"title":"Burf/tfdetection","url":"https://github.com/Burf/tfdetection"},{"title":"sauravmishra1710/UNet-Plus-Plus---Brain-Tumor-Segmentation","url":"https://github.com/sauravmishra1710/UNet-Plus-Plus---Brain-Tumor-Segmentation"},{"title":"rizalmaulanaa/robustness_of_prob_u_net","url":"https://github.com/rizalmaulanaa/robustness_of_prob_u_net"},{"title":"mrgiovanni/dissertation","url":"https://github.com/mrgiovanni/dissertation"},{"title":"albertsokol/pneumothorax-detection-unet","url":"https://github.com/albertsokol/pneumothorax-detection-unet"},{"title":"reyvaz/steel-defect-segmentation","url":"https://github.com/reyvaz/steel-defect-segmentation"},{"title":"reyvaz/pneumothorax_detection","url":"https://github.com/reyvaz/pneumothorax_detection"},{"title":"alexssanchez/unet-app-pucp","url":"https://github.com/alexssanchez/unet-app-pucp"},{"title":"manuelhz/dissertation","url":"https://github.com/manuelhz/dissertation"},{"title":"2023-MindSpore-1/ms-code-118","url":"https://github.com/2023-MindSpore-1/ms-code-118"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/unet-redesigning-skip-connections-to-exploit","title":"UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation","date":"2019-12-11","rows_on_this_dataset":1,"code_links":13,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":1,"samples_unverified":8,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/noise2self-blind-denoising-by-self","title":"Noise2Self: Blind Denoising by Self-Supervision","date":"2019-01-30","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":20,"samples_ran":3,"samples_unverified":17,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/panoptic-segmentation-with-an-end-to-end-cell","title":"Panoptic Segmentation with an End-to-End Cell R-CNN for Pathology Image Analysis","date":"2018-09-28","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/mask-r-cnn","title":"Mask R-CNN","date":"2017-03-20","rows_on_this_dataset":1,"code_links":179,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":140,"samples_ran":42,"samples_unverified":98,"pointer_only_for_licence":23,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/image-to-image-translation-with-conditional","title":"Image-to-Image Translation with Conditional Adversarial Networks","date":"2016-11-21","rows_on_this_dataset":1,"code_links":192,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":122,"samples_ran":14,"samples_unverified":108,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/perceptual-losses-for-real-time-style","title":"Perceptual Losses for Real-Time Style Transfer and Super-Resolution","date":"2016-03-27","rows_on_this_dataset":1,"code_links":80,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":46,"samples_ran":13,"samples_unverified":33,"pointer_only_for_licence":5,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":5,"samples_harvested":337,"samples_ran":73,"samples_unverified":264,"pointer_only_for_licence":31,"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."}