These are generated (synthetic) histology images of colorectal cancer. These images were generated by conditional GANs and are in two classes: MSIH (microsatellite instable high) and nonMSIH. There are two sets: one set with 10K images per class and another one with 75K images per class. All images are RGB, 512x512 px at a resolution of 0.5 micrometers per pixel. For more information, please stay tuned for our upcoming manuscript on www.kather.ai
Software to accompany the manuscript "Deep Learning Generates Xenohistologic Representations of Canc...
In recent years, the area of Medicine and Healthcare has made significant advances with the assistan...
One of the major obstacles in automatic polyp detection during colonoscopy is the lack of labeled po...
Deep learning can detect microsatellite instability (MSI) from routine histology images in colorecta...
Computational histopathology algorithms can interpret very large volumes of data, which can navigate...
Automated synthesis of histology images has several potential applications including the development...
This repository contains trained deep learning models (based on shufflenet) for detecting microsatel...
In this paper, a novel synthetic gastritis image generation method based on a generative adversarial...
This is a set of 11977 image patches of hematoxylin & eosin stained histological samples of human co...
Content The present dataset is linked to a research aimed at discovering the best normalization pip...
<div>The data set has 357 histopathological images of normal tissue and for cancer grades G1, G2, an...
Cancer is one of the dreadfull diseases that persistently challenge biomedical engineering to use el...
Tissue phenotyping of the tumor microenvironment has a decisive role in digital profiling of intra-t...
These are histological images of colorectal cancer, derived from the TCGA database at https://portal...
Colorectal cancer is one of the most common cancers worldwide, accounting for an annual estimated 1....
Software to accompany the manuscript "Deep Learning Generates Xenohistologic Representations of Canc...
In recent years, the area of Medicine and Healthcare has made significant advances with the assistan...
One of the major obstacles in automatic polyp detection during colonoscopy is the lack of labeled po...
Deep learning can detect microsatellite instability (MSI) from routine histology images in colorecta...
Computational histopathology algorithms can interpret very large volumes of data, which can navigate...
Automated synthesis of histology images has several potential applications including the development...
This repository contains trained deep learning models (based on shufflenet) for detecting microsatel...
In this paper, a novel synthetic gastritis image generation method based on a generative adversarial...
This is a set of 11977 image patches of hematoxylin & eosin stained histological samples of human co...
Content The present dataset is linked to a research aimed at discovering the best normalization pip...
<div>The data set has 357 histopathological images of normal tissue and for cancer grades G1, G2, an...
Cancer is one of the dreadfull diseases that persistently challenge biomedical engineering to use el...
Tissue phenotyping of the tumor microenvironment has a decisive role in digital profiling of intra-t...
These are histological images of colorectal cancer, derived from the TCGA database at https://portal...
Colorectal cancer is one of the most common cancers worldwide, accounting for an annual estimated 1....
Software to accompany the manuscript "Deep Learning Generates Xenohistologic Representations of Canc...
In recent years, the area of Medicine and Healthcare has made significant advances with the assistan...
One of the major obstacles in automatic polyp detection during colonoscopy is the lack of labeled po...