Scaling self-supervised learning for histopathology with masked image modeling
Computational pathology is revolutionizing the field of pathology by integrating advanced
computer vision and machine learning technologies into diagnostic workflows. It offers …
computer vision and machine learning technologies into diagnostic workflows. It offers …
Benchmarking self-supervised learning on diverse pathology datasets
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 …
and pathology images are notoriously expensive to annotate. Self-supervised learning has …
Domain-specific optimization and diverse evaluation of self-supervised models for histopathology
Task-specific deep learning models in histopathology offer promising opportunities for
improving diagnosis, clinical research, and precision medicine. However, development of …
improving diagnosis, clinical research, and precision medicine. However, development of …
Self-supervision closes the gap between weak and strong supervision in histology
O Dehaene, A Camara, O Moindrot… - arXiv preprint arXiv …, 2020 - arxiv.org
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 …
supervision: whole-slide images have billions of pixels yet often only one global label. The …
Teacher-student chain for efficient semi-supervised histology image classification
Deep learning shows great potential for the domain of digital pathology. An automated
digital pathology system could serve as a second reader, perform initial triage in large …
digital pathology system could serve as a second reader, perform initial triage in large …
Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology
Artificial intelligence (AI) can extract visual information from histopathological slides and
yield biological insight and clinical biomarkers. Whole slide images are cut into thousands of …
yield biological insight and clinical biomarkers. Whole slide images are cut into thousands of …
Self-supervised driven consistency training for annotation efficient histopathology image analysis
Training a neural network with a large labeled dataset is still a dominant paradigm in
computational histopathology. However, obtaining such exhaustive manual annotations is …
computational histopathology. However, obtaining such exhaustive manual annotations is …
Learning representations with contrastive self-supervised learning for histopathology applications
Unsupervised learning has made substantial progress over the last few years, especially by
means of contrastive self-supervised learning. The dominating dataset for benchmarking self …
means of contrastive self-supervised learning. The dominating dataset for benchmarking self …
A general-purpose self-supervised model for computational pathology
Tissue phenotyping is a fundamental computational pathology (CPath) task in learning
objective characterizations of histopathologic biomarkers in anatomic pathology. However …
objective characterizations of histopathologic biomarkers in anatomic pathology. However …
Self-supervised vision transformers learn visual concepts in histopathology
RJ Chen, RG Krishnan - arXiv preprint arXiv:2203.00585, 2022 - arxiv.org
Tissue phenotyping is a fundamental task in learning objective characterizations of
histopathologic biomarkers within the tumor-immune microenvironment in cancer pathology …
histopathologic biomarkers within the tumor-immune microenvironment in cancer pathology …