Unsupervised Detection of Cancerous Regions in Histology Imagery using Image-to-Image Translation

TL;DR

This paper introduces an unsupervised image-to-image translation framework that effectively detects cancerous regions in histology images, achieving performance close to supervised methods without requiring labeled data.

Contribution

The work presents a novel unsupervised approach using image-to-image translation that outperforms existing methods in detecting cancerous regions in histology imagery.

Findings

Surpasses existing unsupervised methods in accuracy.

Approaches the performance of supervised methods.

Effective in complex biomedical imaging scenarios.

Abstract

Detection of visual anomalies refers to the problem of finding patterns in different imaging data that do not conform to the expected visual appearance and is a widely studied problem in different domains. Due to the nature of anomaly occurrences and underlying generating processes, it is hard to characterize them and obtain labeled data. Obtaining labeled data is especially difficult in biomedical applications, where only trained domain experts can provide labels, which often come in large diversity and complexity. Recently presented approaches for unsupervised detection of visual anomalies approaches omit the need for labeled data and demonstrate promising results in domains, where anomalous samples significantly deviate from the normal appearance. Despite promising results, the performance of such approaches still lags behind supervised approaches and does not provide a one-fits-all solution. In this work, we present an image-to-image translation-based framework that significantly surpasses the performance of existing unsupervised methods and approaches the performance of supervised methods in a challenging domain of cancerous region detection in histology imagery.