Research graph
References from TransBreast-Net: An interpretable vision transformer ensemble for breast cancer histopathology classification. Local targets link to admitted publications; unresolved targets remain external evidence.
Current and future burden of breast cancer: global statistics for 2020 and 2040
10.1016/j.breast.2022.08.010 · 2022 · External reference
A dataset for breast cancer histopathological image classification
10.1109/tbme.2015.2496264 · 2016 · External reference
Machine learning methods for histopathological image analysis
10.1016/j.csbj.2018.01.001 · 2018 · External reference
Convolutional neural network in medical image analysis: a review
10.1007/s11831-023-09898-w · 2023 · External reference
Deep learning approaches to detect breast cancer: a comprehensive review
10.1007/s11042-024-20011-6 · 2025 · External reference
Attention is all you need
2017 · External reference
An image is worth 16x16 words: Transformers for image recognition at scale
2021 · External reference
BACH: grand challenge on breast cancer histology images
10.1016/j.media.2019.05.010 · 2019 · External reference
Transfusion: understanding transfer learning for medical imaging
2019 · External reference
Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning
10.1109/tmi.2016.2528162 · 2016 · External reference
Grad-CAM: visual explanations from deep networks via gradient-based localization
2017 · External reference
A survey on explainable artificial intelligence (XAI): toward medical XAI
10.1109/tnnls.2020.3027314 · 2020 · External reference
TransMIL: transformer based correlated multiple instance learning for whole slide image classification
2021 · External reference
Scaling vision transformers to gigapixel images via hierarchical self-supervised learning
2022 · External reference
Predicting learning achievement using ensemble learning with result explanation
10.1371/journal.pone.0312124 · 2025 · External reference
Training data-efficient image transformers & distillation through attention
2021 · External reference
Swin transformer: hierarchical vision transformer using shifted windows
2021 · External reference
Evaluation and benchmark for biological image segmentation
2008 · External reference
Classification of breast cancer histology images using convolutional neural networks
10.1371/journal.pone.0177544 · 2017 · External reference
A survey on deep learning in medical image analysis
10.1016/j.media.2017.07.005 · 2017 · External reference
Swin transformer: hierarchical vision transformer using shifted windows
2021 · External reference
Going deeper with image transformers
2021 · External reference
TransUNet: transformers make strong encoders for medical image segmentation
2022 · External reference
DecT: deformable cross attention transformer for histopathology image classification
2022 · External reference
Breast cancer detection in histopathological images using Swin transformer
10.3390/math10214109 · 2022 · External reference
Multi-instance learning based transformer for histopathological image classification
2022 · External reference
TransPath: transformer-based self-supervised learning for histopathology image classification
2021 · External reference
HFT-net: hybrid fusion transformer network for multi-source breast cancer classification
10.1109/access.2025.3615654 · 2025 · External reference
A compressed whole-slide transformer for histopathology image analysis
2024 · External reference
TransRAM: transformer-based relational attention model for breast cancer histopathological image classification
2021 · External reference
Histopathological breast cancer detection using BERT and LSTM ensemble
2023 · External reference
HATNet: hierarchical attention network for breast cancer histopathology image classification
2022 · External reference
Deep learning for digital pathology image analysis: a comprehensive tutorial with selected use cases
10.4103/2153-3539.186902 · 2016 · External reference
Swin transformer-based segmentation and multi-scale feature pyramid fusion module for Alzheimer’s disease with machine learning
2023 · External reference
An adaptive fractal image steganography using Mandelbrot and linear congruent generator
2023 · External reference
Artificial intelligence applications in medical devices for personalized health care solutions: systematic review
2026 · External reference
Lightweight dual-stream multi-scale feature fusion medical image multi-disease adaptation classification network based on guided enhancement
10.1016/j.engappai.2025.113083 · 2026 · External reference
G2Grad-CAMRL: an object detection and interpretation model based on gradient-weighted class activation mapping and reinforcement learning in remote sensing images
10.1109/jstars.2023.3241405 · 2023 · External reference
Transparency in diagnosis: unveiling the power of deep learning and explainable AI for medical image interpretation
10.1007/s13369-024-09896-5 · 2025 · External reference
Deep learning with class activation maps for histopathology image analysis
2021 · External reference
STEDNet: Swin transformer-based encoder–decoder network for noise reduction in low-dose CT
10.1002/mp.16249 · 2023 · External reference
Generating image descriptions of rice diseases and pests based on DeiT feature encoder
10.3390/app131810005 · 2023 · External reference
Cait: triple-win compression towards high accuracy, fast inference, and favorable transferability for ViTs
2025 · External reference
Unresolved reference
2021 · External reference
Unresolved reference
External reference
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
10.1007/s11263-019-01228-7 · 2020 · External reference
