The PatchCamelyon benchmark dataset (PCAM)
Bas Veeling

@article{,
title= {The PatchCamelyon benchmark dataset (PCAM)},
doi= {10.1007/978-3-030-00934-2_24},
year= {2018},
keywords= {},
author= {Bas Veeling},
abstract= {The PatchCamelyon benchmark is a new and challenging image classification dataset. It consists of 327.680 color images (96 x 96px) extracted from histopathologic scans of lymph node sections. Each image is annoted with a binary label indicating presence of metastatic tissue. PCam provides a new benchmark for machine learning models: bigger than CIFAR10, smaller than imagenet, trainable on a single GPU.

## Why PCam
Fundamental machine learning advancements are predominantly evaluated on straight-forward natural-image classification datasets. Think MNIST, CIFAR, SVHN. Medical imaging is becoming one of the major applications of ML and we believe it deserves a spot on the list of go-to ML datasets. Both to challenge future work, and to steer developments into directions that are beneficial for this domain.

We think PCam can play a role in this. It packs the clinically-relevant task of metastasis detection into a straight-forward binary image classification task, akin to CIFAR-10 and MNIST. Models can easily be trained on a single GPU in a couple hours, and achieve competitive scores in the Camelyon16 tasks of tumor detection and WSI diagnosis. Furthermore, the balance between task-difficulty and tractability makes it a prime suspect for fundamental machine learning research on topics as active learning, model uncertainty and explainability.

https://github.com/basveeling/pcam/raw/master/pcam.jpg

**Reference:** Veeling, Linmans, Winkens, Cohen and Welling, Rotation Equivariant CNNs for Digital Pathology. MICCAI 2018. https://doi.org/10.1007/978-3-030-00934-2_24 (arXiv: https://arxiv.org/abs/1806.03962)},
terms= {},
license= {https://creativecommons.org/publicdomain/zero/1.0/},
superseded= {},
url= {https://github.com/basveeling/pcam}
}