# Classification and Retrieval of Digital Pathology Scans: A New Dataset

arXiv: https://arxiv.org/abs/1705.07522 · HTML: https://mlsanity.com/paper/1705.07522/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2017.

## Built on by (most cited first, 6 of 6)

- [A Comprehensive Review of Computer-aided Whole-slide Image Analysis: from Datasets to Feature Extraction, Segmentation, Classification, and Detection Approaches](https://mlsanity.com/paper/2102.10553/) (2021)
- [A State-of-the-art Survey of Artificial Neural Networks for Whole-slide Image Analysis:from Popular Convolutional Neural Networks to Potential Visual Transformers](https://mlsanity.com/paper/2104.06243/) (2021)
- [Towards More Transparent and Accurate Cancer Diagnosis with an Unsupervised CAE Approach](https://mlsanity.com/paper/2305.11728/) (2023)
- [Colored Kimia Path24 Dataset: Configurations and Benchmarks with Deep Embeddings](https://mlsanity.com/paper/2102.07611/) (2021)
- [A Similarity Measure of Histopathology Images by Deep Embeddings](https://mlsanity.com/paper/2107.13703/) (2021)
- [Gram Barcodes for Histopathology Tissue Texture Retrieval](https://mlsanity.com/paper/2111.15519/) (2021)

## Abstract (arXiv)

In this paper, we introduce a new dataset, 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 1000×1000 (0.5mm×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.