Synthetic dataset accompanying Neural Image Compression for Gigapixel Histopathology Image Analysis

This dataset was used to develop and evaluate the main method proposed in the paper "Neural Image Compression for Gigapixel Histopathology Image Analysis" published in IEEE Transactions on Pattern Analysis and Machine Intelligence with DOI 10.1109/TPAMI.2019.2936841. Please refer to the paper for a detailed description of the dataset.

The dataset consists of a set of 50000 images and 50000 associated ground truth masks, distributed into training and test partitions. The name of each file follows the pattern "{id}_{tilted_label}_{nontilted_label}_{tilted_size}_{nontilted_size}_{kind}.png" where:
  * id: unique identifier within each partition.
  * tilted_label: image-level label corresponding to the tilted rectangle.
  * nontilted_label: image-level label corresponding to the non-tilted rectangle.
  * tilted_size: longest size of the tilted rectangle.
  * nontilted_size: longest size of the non-tilted rectangle.
  * kind: either "tile" or "mask" image type.

The images are distributed into several data partitions used during cross-validation and fully described in "mnist_folds_set.json". Please rename "mnist_folds_set.json.removethis" into "mnist_folds_set.json".

The code to recreate this dataset can be found in https://github.com/davidtellez/neural-image-compression.