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

arXiv: https://arxiv.org/abs/2406.17670 · HTML: https://mlsanity.com/paper/2406.17670/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 49 references that are in the map.
- First posted on arXiv 25 Jun 2024.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification](https://mlsanity.com/paper/2103.14899/) (2021)
- [CCNet: Criss-Cross Attention for Semantic Segmentation](https://mlsanity.com/paper/1811.11721/) (2018)
- [ViT-CoMer: Vision Transformer with Convolutional Multi-scale Feature Interaction for Dense Predictions](https://mlsanity.com/paper/2403.07392/) (2024)
- [ViTO: Vision Transformer-Operator](https://mlsanity.com/paper/2303.08891/) (2023)
- [CKD-TransBTS: Clinical Knowledge-Driven Hybrid Transformer with Modality-Correlated Cross-Attention for Brain Tumor Segmentation](https://mlsanity.com/paper/2207.07370/) (2022)
- [Sense Less, Generate More: Pre-training LiDAR Perception with Masked Autoencoders for Ultra-Efficient 3D Sensing](https://mlsanity.com/paper/2406.07833/) (2024)
- [The Correlations of Scene Complexity, Workload, Presence, and Cybersickness in a Task-Based VR Game](https://mlsanity.com/paper/2403.19019/) (2024)
- [Vehicle Routing Problem for Urban and Grey Zones Considering Heterogeneous Fleets and Carpooling](https://mlsanity.com/paper/2312.00905/) (2023)
- [Application of Deep Learning for Factor Timing in Asset Management](https://mlsanity.com/paper/2404.18017/) (2024)
- [Hierarchical SegNet with Channel and Context Attention for Accurate Lung Segmentation in Chest X-ray Images](https://mlsanity.com/paper/2405.12318/) (2024)
- [Addressing The Knapsack Challenge Through Cultural Algorithm Optimization](https://mlsanity.com/paper/2401.03324/) (2023)
- [COVID-19 Detection Based on Blood Test Parameters using Various Artificial Intelligence Methods](https://mlsanity.com/paper/2404.02348/) (2024)

## Built on by (most cited first, 3 of 3)

- [Advancing Efficient Brain Tumor Multi-Class Classification -- New Insights from the Vision Mamba Model in Transfer Learning](https://mlsanity.com/paper/2410.21872/) (2024)
- [Brain Tumor Identification using Improved YOLOv8](https://mlsanity.com/paper/2502.03746/) (2025)
- [ResLink: A Novel Deep Learning Architecture for Brain Tumor Classification with Area Attention and Residual Connections](https://mlsanity.com/paper/2508.17259/) (2025)

## Abstract (arXiv)

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.