{"url":"/dataset/brats-2015-1","name":"BraTS 2015","full_name":"BraTS 2015","description_markdown":"The **BraTS 2015** dataset is a dataset for brain tumor image segmentation. It consists of 220 high grade gliomas (HGG) and 54 low grade gliomas (LGG) MRIs. The four MRI modalities are T1, T1c, T2, and T2FLAIR. Segmented “ground truth” is provide about four intra-tumoral classes, viz. edema, enhancing tumor, non-enhancing tumor, and necrosis.\r\n\r\nSource: [Brain MRI Tumor Segmentation with Adversarial Networks](https://arxiv.org/abs/1910.02717)\r\nImage Source: [https://sites.google.com/site/braintumorsegmentation/home/brats2015](https://sites.google.com/site/braintumorsegmentation/home/brats2015)","description_withheld":null,"homepage":"https://www.smir.ch/BRATS/Start2015","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","url":"https://www.smir.ch/BRATS/Start2015"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Medical","url":"/datasets/modality/medical"},{"name":"MRI","url":"/datasets/modality/mri"}],"tasks":[{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"},{"name":"Brain Tumor Segmentation","url":"/task/brain-tumor-segmentation","datasets_with_task":"/datasets/task/brain-tumor-segmentation"},{"name":"Tumor Segmentation","url":"/task/tumor-segmentation","datasets_with_task":"/datasets/task/tumor-segmentation"}],"languages":[],"variants":["BRATS-2015","BraTS 2015"],"data_loaders":[{"repo":"https://github.com/BRML/CNNbasedMedicalSegmentation","url":"https://drive.google.com/drive/folders/1tfQLIBM3SxQAcyGhdQULBHjNJarD2666","frameworks":["tf","pytorch","jax"]}],"num_papers_in_archive":69,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/brain-tumor-segmentation-on-brats-2015","task":"Brain Tumor Segmentation","dataset_variant":"BRATS-2015","rows":4,"metrics":["Dice Score"],"first_row_in_archive_order":{"model":"OM-Net + CGAp","paper":"/paper/one-pass-multi-task-networks-with-cross-task","metrics":{"Dice Score":"87%"},"code_links":[{"title":"chenhong-zhou/OM-Net","url":"https://github.com/chenhong-zhou/OM-Net"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"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/autofocus-layer-for-semantic-segmentation","title":"Autofocus Layer for Semantic Segmentation","date":"2018-05-22","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/cnn-based-segmentation-of-medical-imaging","title":"CNN-based Segmentation of Medical Imaging Data","date":"2017-01-11","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/efficient-multi-scale-3d-cnn-with-fully","title":"Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation","date":"2016-03-18","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":12,"samples_ran":0,"samples_unverified":12,"pointer_only_for_licence":0,"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":12,"samples_ran":0,"samples_unverified":12,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":1,"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."}