{"url":"/dataset/brats-2013-1","name":"BraTS 2013","full_name":"BraTS 2013","description_markdown":"BRATS 2013 is a brain tumor segmentation dataset consists of synthetic and real images, where each of them is further divided into high-grade gliomas (HG) and low-grade gliomas (LG). There are 25 patients with both synthetic HG and LG images and 20 patients with real HG and 10 patients with real LG images. For each patient, FLAIR, T1, T2, and post-Gadolinium T1 magnetic resonance (MR) image sequences are available.\r\n\r\nSource: [Learning Fixed Points in Generative Adversarial Networks: From Image-to-Image Translation to Disease Detection and Localization](https://arxiv.org/abs/1908.06965)\r\nImage Source: [https://arxiv.org/pdf/1708.00377.pdf](https://arxiv.org/pdf/1708.00377.pdf)","description_withheld":null,"homepage":"https://www.smir.ch/BRATS/Start2013","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 (non-commercial)","url":"https://www.smir.ch/BRATS/Start2013"},"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-2013","BRATS-2013 leaderboard","BraTS 2013"],"data_loaders":[],"num_papers_in_archive":36,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/brain-tumor-segmentation-on-brats-2013","task":"Brain Tumor Segmentation","dataset_variant":"BRATS-2013","rows":3,"metrics":["Dice Score"],"first_row_in_archive_order":{"model":"Semantic Genesis","paper":"/paper/learning-semantics-enriched-representation","metrics":{"Dice Score":"92.76"},"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"},{"leaderboard":"/sota/brain-tumor-segmentation-on-brats-2013-1","task":"Brain Tumor Segmentation","dataset_variant":"BRATS-2013 leaderboard","rows":2,"metrics":["Dice Score"],"first_row_in_archive_order":{"model":"InputCascadeCNN","paper":"/paper/brain-tumor-segmentation-with-deep-neural","metrics":{"Dice Score":"0.84"},"code_links":[{"title":"naldeborgh7575/brain_segmentation","url":"https://github.com/naldeborgh7575/brain_segmentation"},{"title":"shalabh147/Brain-Tumor-Segmentation-and-Survival-Prediction-using-Deep-Neural-Networks","url":"https://github.com/shalabh147/Brain-Tumor-Segmentation-and-Survival-Prediction-using-Deep-Neural-Networks"},{"title":"jadevaibhav/Brain-Tumor-Segmentation-using-Deep-Neural-networks","url":"https://github.com/jadevaibhav/Brain-Tumor-Segmentation-using-Deep-Neural-networks"},{"title":"AryaKoureshi/Brain-tumor-detection","url":"https://github.com/AryaKoureshi/Brain-tumor-detection"},{"title":"IAmSuyogJadhav/Brainy","url":"https://github.com/IAmSuyogJadhav/Brainy"},{"title":"dijju/mri-cnn","url":"https://github.com/dijju/mri-cnn"},{"title":"AchintyaX/Brain_tumor_segmentation","url":"https://github.com/AchintyaX/Brain_tumor_segmentation"},{"title":"tbymiracle/Brain-Tumor-Segmentation-Paddle","url":"https://github.com/tbymiracle/Brain-Tumor-Segmentation-Paddle"},{"title":"RobinRajSB/Brain-Tumor-Segmentation-Using-CNN","url":"https://github.com/RobinRajSB/Brain-Tumor-Segmentation-Using-CNN"},{"title":"abhi134/Brain_Tumor_Segmentation","url":"https://github.com/abhi134/Brain_Tumor_Segmentation"},{"title":"peteraugustine/seg3","url":"https://github.com/peteraugustine/seg3"},{"title":"ajinas-ibrahim/brain_tumor","url":"https://github.com/ajinas-ibrahim/brain_tumor"},{"title":"mnsv73/brain-tumour-segmentation","url":"https://github.com/mnsv73/brain-tumour-segmentation"},{"title":"yangyucheng000/Paper-3","url":"https://github.com/yangyucheng000/Paper-3/tree/main/msBraVL-master"}]},"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/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/segan-adversarial-network-with-multi-scale","title":"SegAN: Adversarial Network with Multi-scale $L_1$ Loss for Medical Image Segmentation","date":"2017-06-06","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/brain-tumor-segmentation-with-deep-neural","title":"Brain Tumor Segmentation with Deep Neural Networks","date":"2015-05-13","rows_on_this_dataset":2,"code_links":14,"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."}