ArticleHealthcare technology letters2024
A deep fusion-based vision transformer for breast cancer classification.
Article in Healthcare technology letters, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A Hybrid Model for Ultrasound Image-Based Breast Cancer Diagnosis Using EfficientNet-V2 and Vision Transformer.Diagnostics (Basel, Switzerland) · 2026Article
- Magnification-independent breast cancer diagnosis using a GWO-enhanced vision transformer with multi-stage stain normalization.Scientific reports · 2026Article
- AdaptiveInvolutionNet: Spatially-adaptive involution with channel-wise attention for breast MRI tumor classification.PloS one · 2026Article
- Transformers meet CNNs for insights into breast mass classification from histopathological images.Frontiers in artificial intelligence · 2026Article
- A deep fusion-based vision transformer for breast cancer classification.Healthcare technology letters · 2024Article
- Federated Learning for Thoracic Disease Classification Using Convolutional Neural Networks and Differential Privacy.Healthcare technology lettersArticle
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is one of the most common causes of death in women in the modern world. Cancerous tissue detection in histopathological images relies on complex features related to tissue structure and staining properties. Convolutional neural network (CNN) models like ResNet50, Inception-V1, and VGG-16, while useful in many applications, cannot capture the patterns of cell layers and staining properties. Most previous approaches, such as stain normalization and instance-based vision transformers, either miss important features or do not process the whole image effectively. Therefore, a deep fusion-based vision Transformer model (DFViT) that combines CNNs and transformers for better feature extraction is proposed. DFViT captures local and global patterns more effectively by fusing RGB and stain-normalized images. Trained and tested on several datasets, such as BreakHis, breast cancer histology (BACH), and UCSC cancer genomics (UC), the results demonstrate outstanding accuracy, F1 score, precision, and recall, setting a new milestone in histopathological image analysis for diagnosing breast cancer.
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