The PatchCamelyon benchmark dataset (PCAM)

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

PatchCamelyon 基准数据集是一项新颖且极具挑战性的图像分类数据集。该数据集包含从淋巴结切片的组织病理学扫描中提取的 327,680 张彩色图像（96 x 96 像素）。每张图像均附有二元标签，用以指示是否存在转移性组织。PCam 为机器学习模型提供了一个新的基准：其规模大于 CIFAR10，小于 ImageNet，且可在单个 GPU 上进行训练。为何 PCam 至关重要？因为机器学习的根本性进步大多是在直观的自然图像分类数据集上得到评估的。诸如 MNIST、CIFAR、SVHN 等便是此类数据集的典型代表。医学成像正日益成为机器学习的主要应用之一，我们认为它理应列入首选的机器学习数据集之列。这不仅是为了挑战未来的研究工作，也是为了引导这一领域的发展方向，使之更加有利于该领域的实际应用。我们认为 PCam 在此扮演着至关重要的角色。它将临床相关的转移性组织检测任务简化为一种直观的二值图像分类任务，与 CIFAR-10 和 MNIST 类似。