mlsanityData to 14 Oct 2026

BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification

arXiv 2506.14318 ↗First posted 17 Jun 2025eess.IV cs.CVcited by 15

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Abstract (arXiv)

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. datasetlink: https://www.kaggle.com/datasets/briscdataset/brisc2025/

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 25 of its 91 references that are in the map. First posted on arXiv 17 Jun 2025.

Builds on

25 of 91 references.

References · 25

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  12. 2024The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRIcited by 69
13 more
  1. 2021The Federated Tumor Segmentation (FeTS) Challengecited by 50
  2. 2021Boundary-Aware Segmentation Network for Mobile and Web Applicationscited by 42
  3. 2021Enhanced U-Net: A Feature Enhancement Network for Polyp Segmentationcited by 34
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  5. 2023Brain tumor segmentation using synthetic MR images -- A comparison of GANs and diffusion modelscited by 19
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  13. 2024FusionLungNet: Multi-scale Fusion Convolution with Refinement Network for Lung CT Image Segmentationcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 12.

15 papers.

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  1. 2026MedCLIPSeg: Probabilistic Vision-Language Adaptation for Data-Efficient and Generalizable Medical Image Segmentationcited by 4
  2. 2025CE-RS-SBCIT A Novel Channel Enhanced Hybrid CNN Transformer with Residual, Spatial, and Boundary-Aware Learning for Brain Tumor MRI Analysiscited by 3
  3. 2026Do Synthetic Brain MRIs Reliably Improve Tumour Classification? A StyleGAN2-ADA Class-Plane Augmentation Study on BRISC 2025cited by 1
  4. 2025Mask What Matters: Controllable Text-Guided Masking for Self-Supervised Medical Image Analysiscited by 0
  5. 2025S3F-Net: A Multi-Modal Approach to Medical Image Classification via Spatial-Spectral Summarizer Fusion Networkcited by 0
  6. 2026LAW & ORDER: Adaptive Spatial Weighting for Medical Diffusion and Segmentationcited by 0
  7. 2026LoGSAM: Parameter-Efficient Cross-Modal Grounding for MRI Segmentationcited by 0
  8. 2026Evi-Steer: Learning to Steer Biomedical Vision-Language Models through Efficient and Generalizable Evidential Tuningcited by 0
  9. 2026Bridging Topology and Deep Representation Learning: A TDA-ViT Fusion Model for Four-Class Brain Tumor Classificationcited by 0
  10. 2026Enhancing MedSAM with a Lightweight Box Predictor for Medical Image Segmentationcited by 0
  11. 2026BTI-Net: Bidirectional Decoder-Level Task Interaction via Uncertainty-Aware Gating for Multi-Task Medical Image Analysiscited by 0
  12. 2026How Many Labels Are Enough? ALDA: Active Learning Deployment Advisor for Medical Image Classificationcited by 0
3 more
  1. 2026ActiveAugment: Online Active Learning for Augmentation Selection in Deep Learningcited by 0
  2. 2026QINA: Quantum-Inspired Nonlinear Adapters for Pretrained Vision Modelscited by 0
  3. 2026BaT-SAM: Introducing Inductive Bias into SAM via a Mixture of Convolutional Experts for Brain Tumor Segmentationcited by 0