Results 11 to 20 of about 2,155,308 (355)
Microscopy with ultraviolet surface excitation for rapid slide-free histology. [PDF]
Nature Biomedical Engineering, 2017 Histologic examination of tissues is central to the diagnosis and management of neoplasms and many other diseases, and is a foundational technique for preclinical and basic research.
Bishop, John +10 more
core +3 more sources
Modeling Dense Multimodal Interactions Between Biological Pathways and Histology for Survival Prediction [PDF]
Computer Vision and Pattern Recognition, 2023 Integrating whole-slide images (WSIs) and bulk tran-scriptomics for predicting patient survival can improve our understanding of patient prognosis. However, this multi-modal task is particularly challenging due to the different nature of these data: WSIs
Guillaume Jaume +5 more
semanticscholar +1 more source
Pan-Cancer Integrative Histology-Genomic Analysis via Multimodal Deep Learning
Cancer Cell, 2022 Summary The rapidly emerging field of computational pathology has demonstrated promise in developing objective prognostic models from histology images. However, most prognostic models are either based on histology or genomics alone and do not address how
Richard J. Chen +10 more
semanticscholar +1 more source
Nature Methods, 2021 Recent advances in spatially resolved transcriptomics (SRT) technologies have enabled comprehensive characterization of gene expression patterns in the context of tissue microenvironment. To elucidate spatial gene expression variation, we present SpaGCN,
Jian Hu +8 more
semanticscholar +1 more source
Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images [PDF]
Medical Image Anal., 2018 Nuclear segmentation and classification within Haematoxylin & Eosin stained histology images is a fundamental prerequisite in the digital pathology work-flow.
S. Graham +6 more
semanticscholar +1 more source
Deep learning generates synthetic cancer histology for explainability and education [PDF]
npj Precision Oncology, 2022 Artificial intelligence methods including deep neural networks (DNN) can provide rapid molecular classification of tumors from routine histology with accuracy that matches or exceeds human pathologists.
J. Dolezal +22 more
semanticscholar +1 more source
The impact of site-specific digital histology signatures on deep learning model accuracy and bias
Nature Communications, 2021 The Cancer Genome Atlas (TCGA) is one of the largest biorepositories of digital histology. Deep learning (DL) models have been trained on TCGA to predict numerous features directly from histology, including survival, gene expression patterns, and driver ...
F. Howard +12 more
semanticscholar +1 more source
BACH: Grand challenge on breast cancer histology images [PDF]
Medical Image Anal., 2018 Breast cancer is the most common invasive cancer in women, affecting more than 10% of women worldwide. Microscopic analysis of a biopsy remains one of the most important methods to diagnose the type of breast cancer. This requires specialized analysis by
Guilherme Aresta +35 more
semanticscholar +1 more source
Deep learning classification of lung cancer histology using CT images
Scientific Reports, 2021 Tumor histology is an important predictor of therapeutic response and outcomes in lung cancer. Tissue sampling for pathologist review is the most reliable method for histology classification, however, recent advances in deep learning for medical image ...
T. Chaunzwa +8 more
semanticscholar +1 more source
Nature Medicine, 2020 Intraoperative diagnosis is essential for providing safe and effective care during cancer surgery1. The existing workflow for intraoperative diagnosis based on hematoxylin and eosin staining of processed tissue is time, resource and labor intensive2,3 ...
Todd C. Hollon +14 more
semanticscholar +1 more source