# Gram Barcodes for Histopathology Tissue Texture Retrieval

arXiv: https://arxiv.org/abs/2111.15519 · HTML: https://mlsanity.com/paper/2111.15519/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 25 references that are in the map.
- First posted on arXiv 28 Nov 2021.

## Builds on (most cited first, 9 of 9)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [A Neural Algorithm of Artistic Style](https://mlsanity.com/paper/1508.06576/) (2015)
- [Similar Image Search for Histopathology: SMILY](https://mlsanity.com/paper/1901.11112/) (2019)
- [Pan-Cancer Diagnostic Consensus Through Searching Archival Histopathology Images Using Artificial Intelligence](https://mlsanity.com/paper/1911.08736/) (2019)
- [Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis](https://mlsanity.com/paper/1802.00752/) (2018)
- [Deep Barcodes for Fast Retrieval of Histopathology Scans](https://mlsanity.com/paper/1805.08833/) (2018)
- [Computer-aided diagnosis in histopathological images of the endometrium using a convolutional neural network and attention mechanisms](https://mlsanity.com/paper/1904.10626/) (2019)
- [Classification and Retrieval of Digital Pathology Scans: A New Dataset](https://mlsanity.com/paper/1705.07522/) (2017)

## Abstract (arXiv)

Recent advances in digital pathology have led to the need for Histopathology Image Retrieval (HIR) systems that search through databases of biopsy images to find similar cases to a given query image. These HIR systems allow pathologists to effortlessly and efficiently access thousands of previously diagnosed cases in order to exploit the knowledge in the corresponding pathology reports. Since HIR systems may have to deal with millions of gigapixel images, the extraction of compact and expressive image features must be available to allow for efficient and accurate retrieval. In this paper, we propose the application of Gram barcodes as image features for HIR systems. Unlike most feature generation schemes, Gram barcodes are based on high-order statistics that describe tissue texture by summarizing the correlations between different feature maps in layers of convolutional neural networks. We run HIR experiments on three public datasets using a pre-trained VGG19 network for Gram barcode generation and showcase highly competitive results.