Artificial intelligence for digital and computational pathology
Advances in digitizing tissue slides and the fast-paced progress in artificial intelligence,
including deep learning, have boosted the field of computational pathology. This field holds …
including deep learning, have boosted the field of computational pathology. This field holds …
[HTML][HTML] Recent advances of deep learning for computational histopathology: principles and applications
Simple Summary The histopathological image is widely considered as the gold standard for
the diagnosis and prognosis of human cancers. Recently, deep learning technology has …
the diagnosis and prognosis of human cancers. Recently, deep learning technology has …
Visual language pretrained multiple instance zero-shot transfer for histopathology images
Contrastive visual language pretraining has emerged as a powerful method for either
training new language-aware image encoders or augmenting existing pretrained models …
training new language-aware image encoders or augmenting existing pretrained models …
[HTML][HTML] Fast and scalable search of whole-slide images via self-supervised deep learning
The adoption of digital pathology has enabled the curation of large repositories of gigapixel
whole-slide images (WSIs). Computationally identifying WSIs with similar morphologic …
whole-slide images (WSIs). Computationally identifying WSIs with similar morphologic …
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 …
Histopathology whole slide image analysis with heterogeneous graph representation learning
Graph-based methods have been extensively applied to whole slide histopathology image
(WSI) analysis due to the advantage of modeling the spatial relationships among different …
(WSI) analysis due to the advantage of modeling the spatial relationships among different …
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 …
Survival prediction across diverse cancer types using neural networks
X Yan, W Wang, M Xiao, Y Li, M Gao - Proceedings of the 2024 7th …, 2024 - dl.acm.org
Gastric cancer and Colon adenocarcinoma represent widespread and challenging
malignancies with high mortality rates and complex treatment landscapes. In response to the …
malignancies with high mortality rates and complex treatment landscapes. In response to the …
Lnpl-mil: Learning from noisy pseudo labels for promoting multiple instance learning in whole slide image
Abstract Gigapixel Whole Slide Images (WSIs) aided patient diagnosis and prognosis
analysis are promising directions in computational pathology. However, limited by …
analysis are promising directions in computational pathology. However, limited by …