{"url":"/dataset/brats-2017-1","name":"BraTS 2017","full_name":"BraTS 2017","description_markdown":"The BRATS2017 dataset. It contains 285 brain tumor MRI scans, with four MRI modalities as T1, T1ce, T2, and Flair for each scan. The dataset also provides full masks for brain tumors, with labels for ED, ET, NET/NCR. The segmentation evaluation is based on three tasks: WT, TC and ET segmentation.\r\n\r\nSource: [Scribble-based Hierarchical Weakly Supervised Learning for Brain Tumor Segmentation](https://arxiv.org/abs/1911.02014)\r\nImage Source: [https://www.google.com/search?q=A+Modified+U-Net+Convolutional+Network+Featuring+a+Nearest-neighbor+Re-sampling-based+Elastic-Transformation+for+Brain+Tissue+Characterization+and+Segmentation&oq=A+Modified+U-Net+Convolutional+Network+Featuring+a+Nearest-neighbor+Re-sampling-based+Elastic-Transformation+for+Brain+Tissue+Characterization+and+Segmentation&aqs=chrome..69i57j69i64l3.296j0j4&sourceid=chrome&ie=UTF-8](https://www.google.com/search?q=A+Modified+U-Net+Convolutional+Network+Featuring+a+Nearest-neighbor+Re-sampling-based+Elastic-Transformation+for+Brain+Tissue+Characterization+and+Segmentation&oq=A+Modified+U-Net+Convolutional+Network+Featuring+a+Nearest-neighbor+Re-sampling-based+Elastic-Transformation+for+Brain+Tissue+Characterization+and+Segmentation&aqs=chrome..69i57j69i64l3.296j0j4&sourceid=chrome&ie=UTF-8)","description_withheld":null,"homepage":"https://www.med.upenn.edu/sbia/brats2017/data.html","introduced_date":"2015-01-01","introduced_date_note":null,"introduced_by":{"paper":null,"title":"The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)","first_author":null,"url":"https://doi.org/10.1109/TMI.2014.2377694"},"license":{"name":"Custom (attribution)","url":"https://www.med.upenn.edu/sbia/brats2017/data.html#:~:text=data%20usage%20agreement"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Medical","url":"/datasets/modality/medical"},{"name":"MRI","url":"/datasets/modality/mri"}],"tasks":[{"name":"Brain Tumor Segmentation","url":"/task/brain-tumor-segmentation","datasets_with_task":"/datasets/task/brain-tumor-segmentation"}],"languages":[],"variants":["BRATS-2017 val","BraTS 2017"],"data_loaders":[],"num_papers_in_archive":77,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/brain-tumor-segmentation-on-brats-2017-val","task":"Brain Tumor Segmentation","dataset_variant":"BRATS-2017 val","rows":3,"metrics":["Dice Score"],"first_row_in_archive_order":{"model":"SegFormer3D","paper":"/paper/segformer3d-an-efficient-transformer-for-3d","metrics":{"Dice Score":"0.9096"},"code_links":[{"title":"osupcvlab/segformer3d","url":"https://github.com/osupcvlab/segformer3d"},{"title":"MindSpore-scientific/code-7","url":"https://github.com/MindSpore-scientific/code-7/tree/main/SegFormer"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/segformer3d-an-efficient-transformer-for-3d","title":"SegFormer3D: an Efficient Transformer for 3D Medical Image Segmentation","date":"2024-04-15","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/one-pass-multi-task-networks-with-cross-task","title":"One-pass Multi-task Networks with Cross-task Guided Attention for Brain Tumor Segmentation","date":"2019-06-05","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/automatic-brain-tumor-segmentation-using-1","title":"Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks","date":"2017-09-01","rows_on_this_dataset":1,"code_links":7,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":0,"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":2,"samples_harvested":4,"samples_ran":4,"samples_unverified":0,"pointer_only_for_licence":2,"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."}