consep数据集，用于病理研究。包括验证集，测试集。

Colorectal Nuclear Segmentation and Phenotypes (CoNSeP) Dataset ---------------------------------------------------------------------------------------------------- Overview: This dataset was first used in our paper named, "HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images" If using any part of this dataset or the code associated, you must give appropriate citation to our paper, published in Medical Image Analysis. ---------------------------------------------------------------------------------------------------- Dataset Description: Each ground truth file is stored as a .mat file, with the keys: 'inst_map' 'type_map' 'inst_type' 'inst_centroid' 'inst_map' is a 1000x1000 array containing a unique integer for each individual nucleus. i.e the map ranges from 0 to N, where 0 is the background and N is the number of nuclei. 'type_map' is a 1000x1000 array where each pixel value denotes the class of that pixel. The map ranges from 0 to 7, where 7 is the total number of classes in CoNSeP. 'inst_type' is a Nx1 array, indicating the type of each instance (in order of inst_map ID) 'inst_centroid' is a Nx2 array, giving the x and y coordinates of the centroids of each instance (in order of inst map ID). Note, 'inst_type' and 'inst_centroid' are only used while computing the classification statistics. The values within the class map indicate the category of each nucleus. Class values: 1 = other 2 = inflammatory 3 = healthy epithelial 4 = dysplastic/malignant epithelial 5 = fibroblast 6 = muscle 7 = endothelial In our paper we combine classes 3 & 4 into the epithelial class and 5,6 & 7 into the spindle-shaped class. Total number of nuclei = 24,319 Note, as of 14/03/2020, we switched the GT from .npy files to .mat files for consistency with the Github repo ------------------------------------------------------------------------------------------