25 datasets found

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 dataset used in the paper for colorectal cancer tissue detection, consisting of H&E stained whole slide images (WSIs) of different tissue types.

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.

The PanNuke dataset comprises H&E stained images with a resolution of 256×256 pixels, totaling 7,904 images from 19 different tissue types.

The dataset used in the paper for whole slide image (WSI) classification, which is a type of digital pathology. The dataset consists of histopathology and cytopathology images,...

The ACROBAT 2022 Challenge: Automatic Registration Of Breast Cancer Tissue

Unsupervised domain adaptation for nuclei instance segmentation in histopathology images

The MoNuSeg dataset is published for the Multi-organ Nuclei Segmentation challenge in MICCAI 2018. The training dataset consists of 30 images generated from multiple organs...

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.

The dataset contains 100 images obtained from tissues of either normal or malignant patient tissues.

CoNSeP dataset consists of 41 H&E stained image tiles, each of size 1000x1000 pixels at 40x objective magnification, with annotations of seven types of nuclei: malignant,...