20 datasets found

Novel Local Radiomic Bayesian Classifiers for Non-invasive Prediction of MGMT Methylation Status in Glioblastoma

The Multimodal Brain Tumor Segmentation Challenge 2020 dataset was used as our primary dataset for brain tumor classification and segmentation.

The Brain Tumor Segmentation (BraTS) Challenge 2021 dataset, which provides a valuable resource for addressing challenges specific to resource-limited settings, particularly the...

The SMU-Net dataset is not explicitly mentioned in the paper, but it is used as a benchmark for the proposed method.

The BraTS 2020 challenge dataset is a multimodal MRI brain tumor segmentation dataset. It contains 369 subjects with 4 MRI modalities (T2 weighted FLAIR, T1 weighted, T1...

Automatic segmentation of brain tumors is an essential but challenging step for extracting quantitative imaging biomarkers for accurate tumor detection, diagnosis, prognosis,...

Multi-parametric MRI scans from 2000 patients were used for BraTS2021, 1251 of which were provided with segmentation labels to the participants for developing their algorithms,...

The dataset contains 293 HGG and 76 LGG pre-operative scans in four MRI modalities, which are T1, T2, T1c and FLAIR.

Fast and accurate reconstruction of magnetic resonance (MR) images from under-sampled data is important in many clinical applications. In recent years, deep learning-based...

The multimodal brain tumor image segmentation benchmark (brats).

The BraTS2020 dataset is a widely-used benchmark for brain tumor segmentation. It contains 369 glioma patient samples with two glioma grades (LGG and HGG).

The TCIA-Glioma dataset is a multi-modal MRI scans dataset for brain tumor segmentation.

The rsna-asnr-miccai brats 2021 benchmark dataset is a multi-modal MRI scans dataset for brain tumor segmentation.

Anomaly detection is the process of identifying atypical data samples that significantly deviate from the majority of the dataset. In the realm of clinical screening and...

The BraTS2021 dataset is an important resource in the field of brain tumor segmentation, consisting of multi-modal magnetic resonance imaging (MRI) data, including four imaging...

The BraTS2018 database is a continually evolving database with a total of 285 glioblastoma or low-grade gliomas subjects, comprising three consecutive subsets, i.e., 30 subjects...

The BRATS 2018, 2019 and 2020 datasets are used for training and evaluation of the proposed 3D UNet model for brain tumor segmentation.

The BRATS18 multi-modal brain tumor dataset is used for training and testing the proposed TC-MGAN model.

Brain Tumor Segmentation dataset contains images of brain tumors from MRI scans. The dataset is used for automatic segmentation of brain tumors.

The Brain Tumor Segmentation (BraTS) 2019 dataset provides 335 training subjects, 125 validation subjects and 167 testing ones, each with four MRI modality sequences (T1, T1ce,...