A large-scale labeled dataset is a key factor for the success of supervised deep learning in
histopathological image analysis. However, exhaustive annotation requires a careful visual …

While high-resolution pathology images lend themselves well to 'data hungry'deep learning
algorithms, obtaining exhaustive annotations on these images for learning is a major …

A large-scale and well-annotated dataset is a key factor for the success of deep learning in
medical image analysis. However, assembling such large annotations is very challenging …

Training a neural network with a large labeled dataset is still a dominant paradigm in
computational histopathology. However, obtaining such exhaustive manual annotations is …

One of the biggest challenges for applying machine learning to histopathology is weak
supervision: whole-slide images have billions of pixels yet often only one global label. The …

Computational pathology can lead to saving human lives, but models are annotation hungry
and pathology images are notoriously expensive to annotate. Self-supervised learning has …

Although convolutional neural networks have achieved tremendous success on
histopathology image classification, they usually require large-scale clean annotated data …

Histopathological images contain abundant phenotypic information and pathological
patterns, which are the gold standards for disease diagnosis and essential for the prediction …

Progress in digital pathology is hindered by high-resolution images and the prohibitive cost
of exhaustive localized annotations. The commonly used paradigm to categorize pathology …

Tissue phenotyping is a fundamental task in learning objective characterizations of
histopathologic biomarkers within the tumor-immune microenvironment in cancer pathology …