ArticleDiagnostics (Basel, Switzerland)2022
ETECADx: Ensemble Self-Attention Transformer Encoder for Breast Cancer Diagnosis Using Full-Field Digital X-ray Breast Images.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 2 of them syntheses that pooled it.
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Who cites it
36 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Application of deep learning technology in breast cancer: a systematic review of segmentation, detection, and classification approaches.Biomedical engineering online · 2026Pooled it
- A systematic literature review on mammography: deep learning techniques for breast cancer detection with global and Asian perspectives.BMC cancer · 2025Pooled it
- Breast Cancer Knowledge and Screening Barriers Among Female University Students of Pakistan.International journal of environmental research and public health · 2026Article
- Addressing Clinical Ambiguity in Breast Density Assessment: A Hybrid Multi-View Deep Learning Framework for BI-RADS B vs. C Classification.Diagnostics (Basel, Switzerland) · 2026Article
- A Robustness-Oriented Quantum-Classical Hybrid Machine Learning Pipeline for Breast Cancer Diagnosis: External Validation, Explainability, and Rigorous Benchmarking in the NISQ Era.Diagnostics (Basel, Switzerland) · 2026Article
- Improving IoT security through an explainable hybrid CNN-transformer model and federated learning.Scientific reports · 2026Article
- BC-SwinNet: Swin transformer and CNN with multi-objective optimization for multi-class breast cancer detection using Histopathological images.Scientific reports · 2026Article
- A Deep Learning Model Integrating Ultrasound Images and Multidimensional Clinical Information for Differentiating Benign and Malignant Non-Mass Lesions.Diagnostics (Basel, Switzerland) · 2026Article
- Deep learning-based breast cancer detection with customized ensemble attention.Scientific reports · 2026Article
- Hybrid diagnostic framework for bone cancer detection using deep learning and radiomics analysis.Scientific reports · 2026Article
- A Vision Transformer-Based Deep Learning Framework for Patient-Level Classification of Acute Pancreatitis and Normal Pancreas Using Computed Tomography.Diagnostics (Basel, Switzerland) · 2026Article
- Toward general AI harmonization with real mammogram imaging breast cancer dataset.BMC research notes · 2026Article
- A Data-Efficient Machine Learning Approach for Breast Ultrasound Lesion Classification Integrating Image-Derived Features and Sonographic Descriptors.Diagnostics (Basel, Switzerland) · 2026Article
- An Interpretable Ensemble Transformer Framework for Breast Cancer Detection in Ultrasound Images.Diagnostics (Basel, Switzerland) · 2026Article
- Integrating Large Language Models with Deep Learning for Breast Cancer Treatment Decision Support.Diagnostics (Basel, Switzerland) · 2026Article
- Predicting tourism growth in Saudi Arabia with machine learning models for vision 2030 perspective.Scientific reports · 2026Article
- Clinically Aware Learning: Ordinal Loss Improves Medical Image Classifiers.Journal of clinical medicine · 2026Article
- Cross-attention guided multi-modal network for breast ultrasound diagnosis incorporating objective clinical semantics.Frontiers in medicine · 2026Article
- LENS: A mammography-specific hybrid CNN-Transformer with lesion-aware evidence modeling.PloS one · 2026Article
- Explainable Mammogram Analysis with EfficientNetV2 and Grad-CAM++ for Robust Cancer Diagnosis.Diagnostics (Basel, Switzerland) · 2025Article
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6 authors.
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Abstract
Early detection of breast cancer is an essential procedure to reduce the mortality rate among women. In this paper, a new AI-based computer-aided diagnosis (CAD) framework called ETECADx is proposed by fusing the benefits of both ensemble transfer learning of the convolutional neural networks as well as the self-attention mechanism of vision transformer encoder (ViT). The accurate and precious high-level deep features are generated via the backbone ensemble network, while the transformer encoder is used to diagnose the breast cancer probabilities in two approaches:
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