In this paper, we introduce a new dataset, \textbf{Kimia Path24}, for image
classification and retrieval in digital pathology. We use the whole scan images
of 24 different tissue textures to generate 1,325 test patches of size
1000$\times$1000 (0.5mm$\times$0.5mm). Training data can be generated according
to preferences of algorithm designer and can range from approximately 27,000 to
over 50,000 patches if the preset parameters are adopted. We propose a compound
patch-and-scan accuracy measurement that makes achieving high accuracies quite
challenging. In addition, we set the benchmarking line by applying LBP,
dictionary approach and convolutional neural nets (CNNs) and report their
results. The highest accuracy was 41.80\% for CNN.

本文介绍了一个新的数据集 “Kimia Path24”，该数据集可用于数字病理学中的图像分类和检索，并通过使用 24 种不同的组织纹理的全扫描图像生成了 1,325 个大小为 1000x1000 的测试补丁。通过 LBP，字典方法和卷积神经网络等方法进行了基准测试，结果表明卷积神经网络获得了最高的准确性，41.8％。