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] Computational pathology: a survey review and the way forward
Abstract Computational Pathology (CPath) is an interdisciplinary science that augments
developments of computational approaches to analyze and model medical histopathology …
developments of computational approaches to analyze and model medical histopathology …
Multimodal co-attention transformer for survival prediction in gigapixel whole slide images
Survival outcome prediction is a challenging weakly-supervised and ordinal regression task
in computational pathology that involves modeling complex interactions within the tumor …
in computational pathology that involves modeling complex interactions within the tumor …
Graph deep learning for the characterization of tumour microenvironments from spatial protein profiles in tissue specimens
Multiplexed immunofluorescence imaging allows the multidimensional molecular profiling of
cellular environments at subcellular resolution. However, identifying and characterizing …
cellular environments at subcellular resolution. However, identifying and characterizing …
A graph-transformer for whole slide image classification
Deep learning is a powerful tool for whole slide image (WSI) analysis. Typically, when
performing supervised deep learning, a WSI is divided into small patches, trained and the …
performing supervised deep learning, a WSI is divided into small patches, trained and the …
Derivation of prognostic contextual histopathological features from whole-slide images of tumours via graph deep learning
Methods of computational pathology applied to the analysis of whole-slide images (WSIs) do
not typically consider histopathological features from the tumour microenvironment. Here …
not typically consider histopathological features from the tumour microenvironment. Here …
Whole slide images are 2d point clouds: Context-aware survival prediction using patch-based graph convolutional networks
Cancer prognostication is a challenging task in computational pathology that requires
context-aware representations of histology features to adequately infer patient survival …
context-aware representations of histology features to adequately infer patient survival …
[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 …
Region of interest (ROI) selection using vision transformer for automatic analysis using whole slide images
Selecting regions of interest (ROI) is a common step in medical image analysis across all
imaging modalities. An ROI is a subset of an image appropriate for the intended analysis …
imaging modalities. An ROI is a subset of an image appropriate for the intended analysis …
NuCLS: A scalable crowdsourcing approach and dataset for nucleus classification and segmentation in breast cancer
M Amgad, LA Atteya, H Hussein, KH Mohammed… - …, 2022 - academic.oup.com
Background Deep learning enables accurate high-resolution mapping of cells and tissue
structures that can serve as the foundation of interpretable machine-learning models for …
structures that can serve as the foundation of interpretable machine-learning models for …