ArticleScientific data2026
BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification.
Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- A Hybrid Approach for Brain-Tumor Detection and Classification from MRI Images.Journal of imaging · 2026Article
- CAE-BrainNet: a statistically validated class-adaptive attention ensemble model for explainable brain tumor classification from MRI.Brain informatics · 2026Article
- Region-Wise Bézier Intensity Augmentation for Domain-Generalized Brain Tumor Segmentation with a Mamba U-Net.Journal of clinical medicine · 2026Article
- Nested Attention Network for Robust Medical Image Segmentation Under Digital Watermarking.Biomimetics (Basel, Switzerland) · 2026Article
- SwiftMSeg: lightweight multi-scale local-global context modeling with transformer for medical image segmentation.Scientific reports · 2026Article
- Brain Tumor Classification and Segmentation in MR Images Using EfficientNet and U-Net++ Models.Diagnostics (Basel, Switzerland) · 2026Article
- Lightweight Multi-Scale Framework for Human Pose and Action Classification.Sensors (Basel, Switzerland) · 2026Article
- Evaluating the detection of small brain lesions in magnetic resonance using deep learning.Frontiers in neuroscience · 2026Article
- NeuroFAIR curator: an AI-assisted framework for metadata enrichment, quality screening, curation prioritization, and FAIR readiness of brain tumor MRI data.Frontiers in neuroinformatics · 2026Article
- A calibration-aware hierarchical CNN-SWIN fusion framework for robust Cross-Dataset brain MRI analysis.Frontiers in neuroscience · 2026Article
- CMRA-DETR: a lightweight and high-accuracy detection framework for MRI-based brain tumor identification.Frontiers in medicine · 2026Article
- A swarm intelligence-driven hybrid framework for brain tumor classification with enhanced deep features.Scientific reports · 2025Article
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Authors and funding
7 authors.
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Abstract
Accurate segmentation and classification of brain tumors from Magnetic Resonance Imaging (MRI) remain key challenges in medical image analysis, primarily due to the lack of high-quality, balanced, and diverse datasets with expert annotations. In this work, we address this gap by introducing BRISC, a dataset designed for brain tumor segmentation and classification tasks, featuring high-resolution segmentation masks. The dataset comprises 6,000 contrast-enhanced T1-weighted MRI scans, which were collated from multiple public datasets that lacked segmentation labels. Our primary contribution is the subsequent expert annotation of these images, performed by certified radiologists and physicians. It includes three major tumor types, namely glioma, meningioma, and pituitary, as well as non-tumorous cases. Each sample includes high-resolution labels and is categorized across axial, sagittal, and coronal imaging planes to facilitate robust model development and cross-view generalization. To demonstrate the utility of the dataset, we provide benchmark results for both tasks using standard deep learning models. The BRISC dataset is made publicly available.
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