Unsupervised Detection of Cancerous Regions in Histology Imagery using Image-to-Image Translation
This addresses the challenge of anomaly detection in biomedical imaging where labeled data is scarce, though it is incremental as it builds on existing unsupervised approaches.
The paper tackled the problem of detecting cancerous regions in histology imagery without labeled data, using an image-to-image translation framework that significantly outperforms existing unsupervised methods and approaches the performance of supervised methods.
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.