Amirreza Fateh Yasin Rezvani Sara Moayedi Sadjad Rezvani Fatemeh Fateh Mansoor Fateh Vahid Abolghasemi

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. In this work, we present a newly developed MRI dataset named BRISC 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. 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 propose a transformer-based model, leveraging a Swin Transformer backbone for multi-scale feature representation, to benchmark both segmentation and classification tasks. This model serves as a benchmark to demonstrate the utility of the BRISC dataset for advancing methodological research in neuro-oncological image analysis. datasetlink: this https URL