# BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification

arXiv: https://arxiv.org/abs/2506.14318 · HTML: https://mlsanity.com/paper/2506.14318/

## 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 (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [The Medical Segmentation Decathlon](https://mlsanity.com/paper/2106.05735/) (2021)
- [LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation](https://mlsanity.com/paper/1707.03718/) (2017)
- [UNeXt: MLP-based Rapid Medical Image Segmentation Network](https://mlsanity.com/paper/2203.04967/) (2022)
- [Pyramid Attention Network for Semantic Segmentation](https://mlsanity.com/paper/1805.10180/) (2018)
- [On the Challenges and Perspectives of Foundation Models for Medical Image Analysis](https://mlsanity.com/paper/2306.05705/) (2023)
- [The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRI](https://mlsanity.com/paper/2405.18368/) (2024)
- [The Federated Tumor Segmentation (FeTS) Challenge](https://mlsanity.com/paper/2105.05874/) (2021)
- [Boundary-Aware Segmentation Network for Mobile and Web Applications](https://mlsanity.com/paper/2101.04704/) (2021)
- [Enhanced U-Net: A Feature Enhancement Network for Polyp Segmentation](https://mlsanity.com/paper/2105.00999/) (2021)
- [Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline](https://mlsanity.com/paper/2411.12814/) (2024)
- [Brain tumor segmentation using synthetic MR images -- A comparison of GANs and diffusion models](https://mlsanity.com/paper/2306.02986/) (2023)
- [The state-of-the-art in Cardiac MRI Reconstruction: Results of the CMRxRecon Challenge in MICCAI 2023](https://mlsanity.com/paper/2404.01082/) (2024)
- [Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge](https://mlsanity.com/paper/2405.09787/) (2024)
- [MMR-Mamba: Multi-Modal MRI Reconstruction with Mamba and Spatial-Frequency Information Fusion](https://mlsanity.com/paper/2406.18950/) (2024)
- [Towards Complex Backgrounds: A Unified Difference-Aware Decoder for Binary Segmentation](https://mlsanity.com/paper/2210.15156/) (2022)
- [Efficient and Accurate Pneumonia Detection Using a Novel Multi-Scale Transformer Approach](https://mlsanity.com/paper/2408.04290/) (2024)
- [MSDNet: Multi-Scale Decoder for Few-Shot Semantic Segmentation via Transformer-Guided Prototyping](https://mlsanity.com/paper/2409.11316/) (2024)
- [Enhancing Few-Shot Image Classification through Learnable Multi-Scale Embedding and Attention Mechanisms](https://mlsanity.com/paper/2409.07989/) (2024)
- [FusionLungNet: Multi-scale Fusion Convolution with Refinement Network for Lung CT Image Segmentation](https://mlsanity.com/paper/2410.15812/) (2024)

## Built on by (most cited first, 15 of 15)

- [MedCLIPSeg: Probabilistic Vision-Language Adaptation for Data-Efficient and Generalizable Medical Image Segmentation](https://mlsanity.com/paper/2602.20423/) (2026)
- [CE-RS-SBCIT A Novel Channel Enhanced Hybrid CNN Transformer with Residual, Spatial, and Boundary-Aware Learning for Brain Tumor MRI Analysis](https://mlsanity.com/paper/2508.17128/) (2025)
- [Do Synthetic Brain MRIs Reliably Improve Tumour Classification? A StyleGAN2-ADA Class-Plane Augmentation Study on BRISC 2025](https://mlsanity.com/paper/2605.23094/) (2026)
- [Mask What Matters: Controllable Text-Guided Masking for Self-Supervised Medical Image Analysis](https://mlsanity.com/paper/2509.23054/) (2025)
- [S3F-Net: A Multi-Modal Approach to Medical Image Classification via Spatial-Spectral Summarizer Fusion Network](https://mlsanity.com/paper/2509.23442/) (2025)
- [LAW & ORDER: Adaptive Spatial Weighting for Medical Diffusion and Segmentation](https://mlsanity.com/paper/2603.04795/) (2026)
- [LoGSAM: Parameter-Efficient Cross-Modal Grounding for MRI Segmentation](https://mlsanity.com/paper/2603.17576/) (2026)
- [Evi-Steer: Learning to Steer Biomedical Vision-Language Models through Efficient and Generalizable Evidential Tuning](https://mlsanity.com/paper/2605.26292/) (2026)
- [Bridging Topology and Deep Representation Learning: A TDA-ViT Fusion Model for Four-Class Brain Tumor Classification](https://mlsanity.com/paper/2606.00927/) (2026)
- [Enhancing MedSAM with a Lightweight Box Predictor for Medical Image Segmentation](https://mlsanity.com/paper/2606.04705/) (2026)
- [BTI-Net: Bidirectional Decoder-Level Task Interaction via Uncertainty-Aware Gating for Multi-Task Medical Image Analysis](https://mlsanity.com/paper/2606.29102/) (2026)
- [How Many Labels Are Enough? ALDA: Active Learning Deployment Advisor for Medical Image Classification](https://mlsanity.com/paper/2608.03511/) (2026)
- [ActiveAugment: Online Active Learning for Augmentation Selection in Deep Learning](https://mlsanity.com/paper/2608.28923/) (2026)
- [QINA: Quantum-Inspired Nonlinear Adapters for Pretrained Vision Models](https://mlsanity.com/paper/2609.29592/) (2026)
- [BaT-SAM: Introducing Inductive Bias into SAM via a Mixture of Convolutional Experts for Brain Tumor Segmentation](https://mlsanity.com/paper/2610.15330/) (2026)

## 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/