A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches
With the development of Computer-aided Diagnosis (CAD) and image scanning techniques,
Whole-slide Image (WSI) scanners are widely used in the field of pathological diagnosis …
Whole-slide Image (WSI) scanners are widely used in the field of pathological diagnosis …
A survey on graph-based deep learning for computational histopathology
With the remarkable success of representation learning for prediction problems, we have
witnessed a rapid expansion of the use of machine learning and deep learning for the …
witnessed a rapid expansion of the use of machine learning and deep learning for the …
[HTML][HTML] Hierarchical graph representations in digital pathology
Cancer diagnosis, prognosis, and therapy response predictions from tissue specimens
highly depend on the phenotype and topological distribution of constituting histological …
highly depend on the phenotype and topological distribution of constituting histological …
[HTML][HTML] SlideGraph+: Whole slide image level graphs to predict HER2 status in breast cancer
Human epidermal growth factor receptor 2 (HER2) is an important prognostic and predictive
factor which is overexpressed in 15–20% of breast cancer (BCa). The determination of its …
factor which is overexpressed in 15–20% of breast cancer (BCa). The determination of its …
Efficient deep learning model for mitosis detection using breast histopathology images
Mitosis detection is one of the critical factors of cancer prognosis, carrying significant
diagnostic information required for breast cancer grading. It provides vital clues to estimate …
diagnostic information required for breast cancer grading. It provides vital clues to estimate …
Quantifying explainers of graph neural networks in computational pathology
Explainability of deep learning methods is imperative to facilitate their clinical adoption in
digital pathology. However, popular deep learning methods and explainability techniques …
digital pathology. However, popular deep learning methods and explainability techniques …
Recent trends in computer assisted diagnosis (CAD) system for breast cancer diagnosis using histopathological images
Breast cancer is one of the common type of cancer in females across the world. An early
detection and diagnosis of breast cancer may reduce the mortality rate to a great extent. To …
detection and diagnosis of breast cancer may reduce the mortality rate to a great extent. To …
Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology
H Sharma, N Zerbe, I Klempert, O Hellwich… - … Medical Imaging and …, 2017 - Elsevier
Deep learning using convolutional neural networks is an actively emerging field in
histological image analysis. This study explores deep learning methods for computer-aided …
histological image analysis. This study explores deep learning methods for computer-aided …
A convolutional neural network and graph convolutional network based framework for classification of breast histopathological images
The spatial correlation among different tissue components is an essential characteristic for
diagnosis of breast cancers based on histopathological images. Graph convolutional …
diagnosis of breast cancers based on histopathological images. Graph convolutional …
Capturing cellular topology in multi-gigapixel pathology images
In computational pathology, multi-gigapixel whole slide images (WSIs) are typically divided
into small patches because of their extremely large size and memory requirements …
into small patches because of their extremely large size and memory requirements …