PatchCamelyon
The PatchCamelyon benchmark is an image classification dataset with 327,680 color images (96 x 96px) extracted from histopathologic scans of lymph node sections. Each image is annotated with a binary label indicating presence of metastatic tissue.
Raw data
Key stats
| Modality | Vision (WSI patches) |
| Task | Binary classification |
| Cancer type | Breast |
| Data size | 8 GB |
| Image dimension | 96 x 96 x 3 |
| Magnification (μm/px) | 10x (1.0) * |
| Files format | h5 |
| Number of images | 327,680 (50% of each class) |
* The slides were acquired and digitized at 2 different medical centers using a 40x objective but under-sampled to 10x to increase the field of view.
Splits
The data source provides train/validation/test splits
| Splits | Train | Validation | Test |
|---|---|---|---|
| #Samples | 262,144 (80%) | 32,768 (10%) | 32,768 (10%) |
Organization
The PatchCamelyon data from zenodo is organized as follows:
├── camelyonpatch_level_2_split_train_x.h5.gz # train images
├── camelyonpatch_level_2_split_train_y.h5.gz # train labels
├── camelyonpatch_level_2_split_valid_x.h5.gz # val images
├── camelyonpatch_level_2_split_valid_y.h5.gz # val labels
├── camelyonpatch_level_2_split_test_x.h5.gz # test images
├── camelyonpatch_level_2_split_test_y.h5.gz # test labels
Download and preprocessing
The dataset class PatchCamelyon supports downloading the data during runtime by setting the init argument download=True.
[!NOTE] In the provided
PatchCamelyon-config files the download argument is set tofalse. To enable automatic download you will need to open the config and setdownload: true.
Labels are provided by source files, splits are given by file names.
Relevant links
Citation
@misc{b_s_veeling_j_linmans_j_winkens_t_cohen_2018_2546921,
author = {B. S. Veeling, J. Linmans, J. Winkens, T. Cohen, M. Welling},
title = {Rotation Equivariant CNNs for Digital Pathology},
month = sep,
year = 2018,
doi = {10.1007/978-3-030-00934-2_24},
url = {https://doi.org/10.1007/978-3-030-00934-2_24}
}