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

Source: [Classification and Retrieval of Digital Pathology Scans: A New Dataset](<https://www.sota2.com/research/paper/68fa63521e209131ce9fe1cb>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:38:15.653Z

Authors: Morteza Babaie, Shivam Kalra, Aditya Sriram, Christopher Mitcheltree, Shujin Zhu, Amin Khatami, Shahryar Rahnamayan, H.R. Tizhoosh

Published: 2017-05-22T00:00:18.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1705.07522>)

[PDF](<https://arxiv.org/pdf/1705.07522.pdf>)