A Similarity Measure of Histopathology Images by Deep Embeddings

TL;DR

This paper introduces a deep learning-based similarity measure for high-resolution histopathology images, enabling efficient content-based comparison and retrieval with high accuracy across multiple magnifications.

Contribution

It extends cosine similarity to matrix form for patch-level deep embeddings at various magnifications, improving image comparison in histopathology.

Findings

Achieved up to 93.18% accuracy in top-5 image retrieval.

Demonstrated effectiveness of multi-magnification embeddings.

Explored embedding reduction for faster similarity measurement.

Abstract

Histopathology digital scans are large-size images that contain valuable information at the pixel level. Content-based comparison of these images is a challenging task. This study proposes a content-based similarity measure for high-resolution gigapixel histopathology images. The proposed similarity measure is an expansion of cosine vector similarity to a matrix. Each image is divided into same-size patches with a meaningful amount of information (i.e., contained enough tissue). The similarity is measured by the extraction of patch-level deep embeddings of the last pooling layer of a pre-trained deep model at four different magnification levels, namely, 1x, 2.5x, 5x, and 10x magnifications. In addition, for faster measurement, embedding reduction is investigated. Finally, to assess the proposed method, an image search method is implemented. Results show that the similarity measure represents the slide labels with a maximum accuracy of 93.18\% for top-5 search at 5x magnification.