# Deep Barcodes for Fast Retrieval of Histopathology Scans

arXiv: https://arxiv.org/abs/1805.08833 · HTML: https://mlsanity.com/paper/1805.08833/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2018.

## Built on by (most cited first, 9 of 9)

- [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)
- [On Image Search in Histopathology](https://mlsanity.com/paper/2401.08699/) (2024)
- [Learning Binary and Sparse Permutation-Invariant Representations for Fast and Memory Efficient Whole Slide Image Search](https://mlsanity.com/paper/2208.13653/) (2022)
- [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)
- [Analysis and Validation of Image Search Engines in Histopathology](https://mlsanity.com/paper/2401.03271/) (2024)
- [Comments on 'Fast and scalable search of whole-slide images via self-supervised deep learning'](https://mlsanity.com/paper/2304.08297/) (2023)
- [SPLICE -- Streamlining Digital Pathology Image Processing](https://mlsanity.com/paper/2404.17704/) (2024)
- [Colored Kimia Path24 Dataset: Configurations and Benchmarks with Deep Embeddings](https://mlsanity.com/paper/2102.07611/) (2021)
- [Gram Barcodes for Histopathology Tissue Texture Retrieval](https://mlsanity.com/paper/2111.15519/) (2021)

## Abstract (arXiv)

We investigate the concept of deep barcodes and propose two methods to generate them in order to expedite the process of classification and retrieval of histopathology images. Since binary search is computationally less expensive, in terms of both speed and storage, deep barcodes could be useful when dealing with big data retrieval. Our experiments use the dataset Kimia Path24 to test three pre-trained networks for image retrieval. The dataset consists of 27,055 training images in 24 different classes with large variability, and 1,325 test images for testing. Apart from the high-speed and efficiency, results show a surprising retrieval accuracy of 71.62% for deep barcodes, as compared to 68.91% for deep features and 68.53% for compressed deep features.