Published November 24, 2020 | Version v1
Journal article Open

Image Synthesis as a Pretext for Unsupervised Histopathological Diagnosis

  • 1. University of Ljubljana


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.



Files (28.5 MB)

Name Size Download all
28.5 MB Preview Download

Additional details

Related works

Is previous version of
10.1007/978-3-030-59520-3_18 (DOI)


iPC – individualizedPaediatricCure: Cloud-based virtual-patient models for precision paediatric oncology 826121
European Commission