{"ID":2895864,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.13372","arxiv_id":"2507.13372","title":"Enhancing Breast Cancer Detection with Vision Transformers and Graph Neural Networks","abstract":"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.","short_abstract":"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 framew...","url_abs":"https://arxiv.org/abs/2507.13372","url_pdf":"https://arxiv.org/pdf/2507.13372v1","authors":"[\"Yeming Cai\",\"Zhenglin Li\",\"Yang Wang\"]","published":"2025-07-11T20:32:48Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\"]","methods":"[\"Vision Transformer\",\"Graph Neural Network\",\"Transformer\"]","has_code":false}