Brain Tumor Classification using Vision Transformer with Selective Cross-Attention Mechanism and Feature Calibration

TL;DR

This paper introduces a novel vision transformer-based method with cross-attention and feature calibration mechanisms to improve brain tumor classification accuracy and efficiency in medical imaging.

Contribution

It proposes new mechanisms, FCM and SCA, that enhance transformer performance and can be integrated into other architectures for medical image analysis.

Findings

Outperforms state-of-the-art methods in accuracy

Achieves higher efficiency in classification tasks

Demonstrates robustness across different datasets

Abstract

Brain tumor classification is a challenging task in medical image analysis. In this paper, we propose a novel approach to brain tumor classification using a vision transformer with a novel cross-attention mechanism. Our approach leverages the strengths of transformers in modeling long-range dependencies and multi-scale feature fusion. We introduce two new mechanisms to improve the performance of the cross-attention fusion module: Feature Calibration Mechanism (FCM) and Selective Cross-Attention (SCA). FCM calibrates the features from different branches to make them more compatible, while SCA selectively attends to the most informative features. Our experiments demonstrate that the proposed approach outperforms other state-of-the-art methods in brain tumor classification, achieving improved accuracy and efficiency. The proposed FCM and SCA mechanisms can be easily integrated into other vision transformer architectures, making them a promising direction for future research in medical image analysis. Experimental results confirm that our approach surpasses existing methods, achieving state-of-the-art performance in brain tumor classification tasks.