Vision transformers for computational histopathology
Computational histopathology is focused on the automatic analysis of rich phenotypic
information contained in gigabyte whole slide images, aiming at providing cancer patients …
information contained in gigabyte whole slide images, aiming at providing cancer patients …
Application of deep learning in histopathology images of breast cancer: a review
With the development of artificial intelligence technology and computer hardware functions,
deep learning algorithms have become a powerful auxiliary tool for medical image analysis …
deep learning algorithms have become a powerful auxiliary tool for medical image analysis …
A ViT-AMC network with adaptive model fusion and multiobjective optimization for interpretable laryngeal tumor grading from histopathological images
The tumor grading of laryngeal cancer pathological images needs to be accurate and
interpretable. The deep learning model based on the attention mechanism-integrated …
interpretable. The deep learning model based on the attention mechanism-integrated …
An enhanced vision transformer with wavelet position embedding for histopathological image classification
Histopathological image classification is a fundamental task in pathological diagnosis
workflow. It remains a huge challenge due to the complexity of histopathological images …
workflow. It remains a huge challenge due to the complexity of histopathological images …
Enhanced Pre-Trained Xception Model Transfer Learned for Breast Cancer Detection
Early detection and timely breast cancer treatment improve survival rates and patients'
quality of life. Hence, many computer-assisted techniques based on artificial intelligence are …
quality of life. Hence, many computer-assisted techniques based on artificial intelligence are …
A survey of Transformer applications for histopathological image analysis: New developments and future directions
CC Atabansi, J Nie, H Liu, Q Song, L Yan… - BioMedical Engineering …, 2023 - Springer
Transformers have been widely used in many computer vision challenges and have shown
the capability of producing better results than convolutional neural networks (CNNs). Taking …
the capability of producing better results than convolutional neural networks (CNNs). Taking …
Transformer-based 3D U-Net for pulmonary vessel segmentation and artery-vein separation from CT images
Transformer-based methods have led to the revolutionizing of multiple computer vision
tasks. Inspired by this, we propose a transformer-based network with a channel-enhanced …
tasks. Inspired by this, we propose a transformer-based network with a channel-enhanced …
Memory-efficient transformer network with feature fusion for breast tumor segmentation and classification task
The analysis of breast cancer using Ultrasounds, Magnetic resonance imaging (MRI), and
Mammogram images plays a crucial role in the early detection of breast tumors in women …
Mammogram images plays a crucial role in the early detection of breast tumors in women …
Hepatocellular carcinoma histopathological images grading with a novel attention-sharing hybrid network based on multi-feature fusion
J Zhang, S Qiu, Q Li, C Zhou, Z Hu, J Weng… - … Signal Processing and …, 2023 - Elsevier
Throughout history until today, hepatocellular carcinoma (HCC) remains one of the most
serious illnesses worldwide due to its high mortality rates. One of the most essential steps to …
serious illnesses worldwide due to its high mortality rates. One of the most essential steps to …
SaTransformer: Semantic‐aware transformer for breast cancer classification and segmentation
J Zhang, Z Zhang, H Liu, S Xu - IET Image Processing, 2023 - Wiley Online Library
Breast cancer classification and segmentation play an important role in identifying and
detecting benign and malignant breast lesions. However, segmentation and classification …
detecting benign and malignant breast lesions. However, segmentation and classification …