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PANDA (Prostate cANcer graDe Assessment)

The PANDA datasets consists of 10,616 whole-slide images of digitized H&E-stained prostate tissue biopsies originating from two medical centers. After the biopsy, the slides were classified into Gleason patterns (3, 4 or 5) based on the architectural growth patterns of the tumor, which are then converted into an ISUP grade on a 0-5 scale.

The Gleason grading system is the most important prognostic marker for prostate cancer and the ISUP grade has a crucial role when deciding how a patient should be treated. However, the system suffers from significant inter-observer variability between pathologists, leading to imperfect and noisy labels.

Source: https://www.kaggle.com/competitions/prostate-cancer-grade-assessment

Raw data

Key stats
Modality Vision (WSI)
Task Multiclass classification (6 classes)
Cancer type Prostate
Data size 347 GB
Image dimension ~20k x 20k x 3
Magnification (μm/px) 20x (0.5) - Level 0
Files format .tiff
Number of images 10,616 (9,555 after removing noisy labels)

Organization

The data prostate-cancer-grade-assessment.zip from kaggle is organized as follows:

prostate-cancer-grade-assessment
├── train_images
│   ├── 0005f7aaab2800f6170c399693a96917.tiff
│   └── ...
├── train_label_masks (not used in eva)
│   ├── 0005f7aaab2800f6170c399693a96917_mask.tiff
│   └── ...
├── train.csv (contains Gleason & ISUP labels)
├── test.csv
├── sample_submission.csv

Download and preprocessing

The PANDA dataset class doesn't download the data during runtime and must be downloaded manually from kaggle.

As done in other studies1 we exclude ~10% of the samples with noisy labels according to kaggle's 6th place solution resulting in a total dataset size of 9555 WSIs.

We then generate random stratified train / validation and test splits using a 0.7 / 0.15 / 0.15 ratio:

Splits Train Validation Test
#Samples 6686 (70%) 1430 (15%) 1439 (15%)

License

CC BY-SA-NC 4.0

References

1 : A General-Purpose Self-Supervised Model for Computational Pathology