Add to ORKGBeyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumor-infiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially ...
Thesis (Master's)--University of Washington, 2023Recent developments in single-cell RNA sequencing (...
We introduce here a novel machine learning (ML) framework to address the issue of the quantitative a...
We introduce here a novel machine learning (ML) framework to address the issue of the quantitative a...
Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images o...
Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TC...
Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TC...
Aims To evaluate if a deep learning algorithm can be trained to identify tumour-infiltrating lymphoc...
This is a dataset of images with or without tumor-infiltrating lymphocytes (TILs). The original imag...
Background: Immune cell infiltration in tumor is an emerging prognostic biomarker in breast cancer. ...
To date, pathological examination of specimens remains largely qualitative. Quantitative measures of...
Despite increasing evidence supporting the clinical relevance of tumour infiltrating lymphocytes (TI...
The tumour microenvironment can provide critical information for disease diagnosis, treatment planni...
Increased levels of tumor-infiltrating lymphocytes (TILs) indicate favorable outcomes in many types ...
In pathology, tissue images are evaluated using a light microscope, relying on the expertise and exp...
In pathology, tissue images are evaluated using a light microscope, relying on the expertise and exp...
Thesis (Master's)--University of Washington, 2023Recent developments in single-cell RNA sequencing (...
We introduce here a novel machine learning (ML) framework to address the issue of the quantitative a...
We introduce here a novel machine learning (ML) framework to address the issue of the quantitative a...
Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images o...
Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TC...
Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TC...
Aims To evaluate if a deep learning algorithm can be trained to identify tumour-infiltrating lymphoc...
This is a dataset of images with or without tumor-infiltrating lymphocytes (TILs). The original imag...
Background: Immune cell infiltration in tumor is an emerging prognostic biomarker in breast cancer. ...
To date, pathological examination of specimens remains largely qualitative. Quantitative measures of...
Despite increasing evidence supporting the clinical relevance of tumour infiltrating lymphocytes (TI...
The tumour microenvironment can provide critical information for disease diagnosis, treatment planni...
Increased levels of tumor-infiltrating lymphocytes (TILs) indicate favorable outcomes in many types ...
In pathology, tissue images are evaluated using a light microscope, relying on the expertise and exp...
In pathology, tissue images are evaluated using a light microscope, relying on the expertise and exp...
Thesis (Master's)--University of Washington, 2023Recent developments in single-cell RNA sequencing (...
We introduce here a novel machine learning (ML) framework to address the issue of the quantitative a...
We introduce here a novel machine learning (ML) framework to address the issue of the quantitative a...