Image Synthesis as a Pretext for Unsupervised Histopathological Diagnosis
This work addresses the problem of detecting anomalies in digital pathology without labeled data, offering a domain-specific incremental advance.
The paper tackled unsupervised anomaly detection in histopathology by adapting high-resolution generative models from face synthesis, achieving multifold improvements in image quality and resolution compared to existing approaches.
Anomaly detection in visual data refers to the problem of differentiating abnormal appearances from normal cases. Supervised approaches have been successfully applied to different domains, but require an abundance of labeled data. Due to the nature of how anomalies occur and their underlying generating processes, it is hard to characterize and label them. Recent advances in deep generative-based models have sparked interest in applying such methods for unsupervised anomaly detection and have shown promising results in medical and industrial inspection domains. In this work we evaluate a crucial part of the unsupervised visual anomaly detection pipeline, that is needed for normal appearance modeling, as well as the ability to reconstruct closest looking normal and tumor samples. We adapt and evaluate different high-resolution state-of-the-art generative models from the face synthesis domain and demonstrate their superiority over currently used approaches on a challenging domain of digital pathology. Multifold improvement in image synthesis is demonstrated in terms of the quality and resolution of the generated images, validated also against the supervised model.