32 datasets found

A dataset of 100,000 histological images of human colorectal cancer and healthy tissue.

The ANHIR dataset consists of 481 image pairs and is split into 230 training and 251 evaluation pairs. For the training image pairs, both source and target landmarks are...

Autofluorescence images of label-free tissue sample can be used to perform micro-structured and multiplexed virtual staining using a deep neural network.

The Breast Cancer Histopathological Image Classification dataset contains images of breast cancer histopathology.

A dataset used for testing the proposed Deep Feature Learning method for histopathology image classification.

A dataset used for training and testing the proposed Deep Feature Learning method for histopathology image classification.

A dataset composed of 35334 breast histopathology images at zoom x5 (1.76 µm per pixel) distributed amongst 23 imbalanced classes, which include both common tumor and benign...

Whole slide image annotation dataset for selective whole slide image annotation

Whole slide image annotation dataset for selective whole slide image annotation

The Atlas of Digital Pathology (ADP) dataset is a collection of histopathological images from different organs, annotated at the patch level.

This dataset is used for nuclei grading of clear cell renal cell carcinoma in histopathological images.

This dataset is used for nuclei instance segmentation on histopathology images. It consists of 30 nuclei images of size 1000 x 1000 with 658 nuclei per image on average.

A dataset used for testing the proposed Deep Feature Learning method for histopathology image classification.

The CATCH dataset is used to test the proposed Style-Extracting Diffusion Models (STEDM) for canine cancer histology image segmentation.

The HER2 dataset is used to test the proposed Style-Extracting Diffusion Models (STEDM) for histopathology image segmentation.

Synthetic generation of three-dimensional cell models from histopathological images

The WSSS4LUAD challenge dataset contains 10,091 patch-level annotations and over 130 million labeled pixels from 87 WSIs of lung adenocarcinoma.

The dataset used in this paper is the TCGA dataset, which contains somatic genomic alterations (SGAs) and differentially expressed genes (DEGs) of 4,468 tumors.

HE-Staining Variation (HEV) dataset is used to train and evaluate the proposed stain normalization method.

Multi-scanner canine cutaneous tumor dataset, used for tumor segmentation.