# Enhancing Breast Cancer Detection with Vision Transformers and Graph Neural Networks

arXiv: https://arxiv.org/abs/2507.13372 · HTML: https://mlsanity.com/paper/2507.13372/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 13 references that are in the map.
- First posted on arXiv 11 Jul 2025.

## Builds on (most cited first, 6 of 6)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Graph Neural Networks in Network Neuroscience](https://mlsanity.com/paper/2106.03535/) (2021)
- [Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Review](https://mlsanity.com/paper/2301.03505/) (2023)
- [More but Correct: Generating Diversified and Entity-revised Medical Response](https://mlsanity.com/paper/2108.01266/) (2021)
- [Multimodal Data Integration for Oncology in the Era of Deep Neural Networks: A Review](https://mlsanity.com/paper/2303.06471/) (2023)
- [A Comprehensive Survey on Graph Summarization with Graph Neural Networks](https://mlsanity.com/paper/2302.06114/) (2023)

## Abstract (arXiv)

Breast cancer is a leading cause of death among women globally, and early detection is critical for improving survival rates. This paper introduces an innovative framework that integrates Vision Transformers (ViT) and Graph Neural Networks (GNN) to enhance breast cancer detection using the CBIS-DDSM dataset. Our framework leverages ViT's ability to capture global image features and GNN's strength in modeling structural relationships, achieving an accuracy of 84.2%, outperforming traditional methods. Additionally, interpretable attention heatmaps provide insights into the model's decision-making process, aiding radiologists in clinical settings.