BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification with Swin-HAFNet

• URL: http://arxiv.org/abs/2506.14318v1
• Date: Tue, 17 Jun 2025 08:56:05 GMT
• Title: BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification with Swin-HAFNet
• Authors: Amirreza Fateh, Yasin Rezvani, Sara Moayedi, Sadjad Rezvani, Fatemeh Fateh, Mansoor Fateh,
• Abstract summary: The dataset comprises 6,000 contrast-enhanced T1-weighted MRI scans annotated by radiologists and physicians.<n>Each sample includes high-resolution labels and is categorized across axial, sagittal, and coronal imaging planes.<n>Our method achieves the highest weighted mean Intersection-over-Union (IoU) of 82.3%, with improvements observed across all tumor categories.
• Score: 1.1545092788508224
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate segmentation and classification of brain tumors from Magnetic Resonance Imaging (MRI) remain key challenges in medical image analysis, largely due to the lack of high-quality, balanced, and diverse datasets. In this work, we present a new curated MRI dataset designed specifically for brain tumor segmentation and classification tasks. The dataset comprises 6,000 contrast-enhanced T1-weighted MRI scans annotated by certified radiologists and physicians, spanning three major tumor types-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 propose a transformer-based segmentation model and benchmark it against established baselines. Our method achieves the highest weighted mean Intersection-over-Union (IoU) of 82.3%, with improvements observed across all tumor categories. Importantly, this study serves primarily as an introduction to the dataset, establishing foundational benchmarks for future research. We envision this dataset as a valuable resource for advancing machine learning applications in neuro-oncology, supporting both academic research and clinical decision-support development. datasetlink: https://www.kaggle.com/datasets/briscdataset/brisc2025/

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