15 datasets found

This dataset is used for simultaneous segmentation and classification of nuclei in multi-tissue histology 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.

The dataset for nuclei segmentation task.

The dataset for nuclei classification, segmentation, and detection tasks.

GLySAC consists of 59 H&E images of size 1000×1000 pixels, and split into 34 train images and 25 test images.

The dataset used in the paper is a real-world dataset for testing the parametric efficiency of quadratic networks.

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 for lymphoma classification, IDC detection, nuclei segmentation, epithelium segmentation, tubule segmentation, lymphocyte detection, and mitosis detection.

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,...

PanNuke dataset consists of 6078 H&E tiles, each of size 256x256, covering 19 different tissue types. It was designed for nuclei instance classification and segmentation and...

Multi-class histopathology nuclei analysis tasks, the lack of training data becomes a main bottleneck for the performance of learning-based methods.

The dataset used for histopathology image segmentation and classification.

The dataset used for histopathology image segmentation and classification.

The MoNuSeg dataset comprises 30 microscopic images from seven organs in the training set, with annotations of 21,623 individual nuclei, and 14 similar images in the test set.