Classification and Retrieval of Digital Pathology Scans: A New Dataset
This provides a new benchmark for digital pathology researchers, but it is incremental as it focuses on dataset creation and baseline results.
The authors introduced a new dataset, Kimia Path24, for image classification and retrieval in digital pathology, and reported a highest accuracy of 41.80% using convolutional neural nets.
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.