# An Unsupervised Learning Tool for Plaque Tissue Characterization in Histopathological Images

## TL;DR

This paper introduces an unsupervised learning tool to analyze carotid plaque images, aiming to improve stroke risk assessment and patient stratification.

## Contribution

The novel contribution is an unsupervised learning method for plaque tissue characterization in histopathological images.

## Key findings

- The proposed method provides qualitative and quantitative tools for analyzing atherosclerotic plaque complexity.
- The code for the analysis is freely available to support further research and application.
- Future work should validate the method's clusters against manual annotations by experts.

## Abstract

Stroke is the second leading cause of death and a major cause of disability around the world, and the development of atherosclerotic plaques in the carotid arteries is generally considered the leading cause of severe cerebrovascular events. In recent years, new reports have reinforced the role of an accurate histopathological analysis of carotid plaques to perform the stratification of affected patients and proceed to the correct prevention of complications. This work proposes applying an unsupervised learning approach to analyze complex whole-slide images (WSIs) of atherosclerotic carotid plaques to allow a simple and fast examination of their most relevant features. All the code developed for the present analysis is freely available. The proposed method offers qualitative and quantitative tools to assist pathologists in examining the complexity of whole-slide images of carotid atherosclerotic plaques more effectively. Nevertheless, future studies using supervised methods should provide evidence of the correspondence between the clusters estimated using the proposed textural-based approach and the regions manually annotated by expert pathologists.