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Image Synthesis as a Pretext for Unsupervised Histopathological Diagnosis

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 12417)

Abstract

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 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 towards 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 modelling, 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.

Keywords

Anomaly detection Unsupervised Deep-learning Generative adversarial networks Image synthesis Digital pathology 

Notes

Acknowledgment

This work was partially supported by the European Commission through the Horizon 2020 research and innovation program under grant 826121 (iPC).

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Faculty of Computer and Information ScienceUniversity of LjubljanaLjubljanaSlovenia
  2. 2.XLAB d.o.o.LjubljanaSlovenia

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