RNA-sequencing of tumour tissue can provide important diagnostic and prognostic information but this is costly and not routinely performed in all clinical settings. Here, the authors show that whole slide histology slides—part of routine care—can be used to predict RNA-sequencing data and thus reduce the need for additional analyses
The highest number of cancer-associated deaths are attributable to metastasis. These include rare ca...
Deep learning (DL) can predict microsatellite instability (MSI) from routine histopathology slides o...
Histopathology plays a vital role in cancer diagnosis, prognosis, and treatment decisions. The whole...
International audienceDeep learning methods for digital pathology analysis are an effective way to a...
Advanced diagnostics are enabling cancer treatments to become increasingly tailored to the individua...
Numerous areas of medical services, including as imaging diagnostics, advanced pathology, emergency ...
Molecular profiling is central in cancer precision medicine but remains costly and is based on tumor...
Thesis (Master's)--University of Washington, 2023Recent developments in single-cell RNA sequencing (...
DeepPT codes in the manuscript "Prediction of cancer treatment response from histopathology images t...
Molecular alterations in cancer can cause phenotypic changes in tumor cells and their microenvironme...
Motivation: Molecular phenotyping by gene expression profiling is central in contemporary cancer res...
Background: Intra-tumour heterogeneity (ITH) causes diagnostic challenges and increases the risk for...
Ribonucleic acid Sequencing (RNA-Seq) analysis is particularly useful for obtaining insights into di...
Abstract Digital analysis of pathology whole-slide images is fast becoming a game changer i...
Interpretability of deep learning is widely used to evaluate the reliability of medical imaging mode...
The highest number of cancer-associated deaths are attributable to metastasis. These include rare ca...
Deep learning (DL) can predict microsatellite instability (MSI) from routine histopathology slides o...
Histopathology plays a vital role in cancer diagnosis, prognosis, and treatment decisions. The whole...
International audienceDeep learning methods for digital pathology analysis are an effective way to a...
Advanced diagnostics are enabling cancer treatments to become increasingly tailored to the individua...
Numerous areas of medical services, including as imaging diagnostics, advanced pathology, emergency ...
Molecular profiling is central in cancer precision medicine but remains costly and is based on tumor...
Thesis (Master's)--University of Washington, 2023Recent developments in single-cell RNA sequencing (...
DeepPT codes in the manuscript "Prediction of cancer treatment response from histopathology images t...
Molecular alterations in cancer can cause phenotypic changes in tumor cells and their microenvironme...
Motivation: Molecular phenotyping by gene expression profiling is central in contemporary cancer res...
Background: Intra-tumour heterogeneity (ITH) causes diagnostic challenges and increases the risk for...
Ribonucleic acid Sequencing (RNA-Seq) analysis is particularly useful for obtaining insights into di...
Abstract Digital analysis of pathology whole-slide images is fast becoming a game changer i...
Interpretability of deep learning is widely used to evaluate the reliability of medical imaging mode...
The highest number of cancer-associated deaths are attributable to metastasis. These include rare ca...
Deep learning (DL) can predict microsatellite instability (MSI) from routine histopathology slides o...
Histopathology plays a vital role in cancer diagnosis, prognosis, and treatment decisions. The whole...