Abstract
Computer-aided screening methods can reduce the burden of manual grading. However, manual grading is still required to provide datasets with annotated pathologies that are used for the development of supervised machine learning based systems of the kind. In this paper we demonstrate a different method, based on unsupervised anomaly detection techniques, that can be exploited to detect and localize pathologies at the pixel-level in retinal images. We introduce a new reconstruction-based model architecture, trained with only healthy retinal images, and leverage it to generate anomaly maps from where the anomalous patterns can be located, which allows to automatically discover the image locations that can potentially be pathological lesions.
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Acknowledgement
This research is supported by the Instituto de Salud Carlos III, Government of Spain and the ERDF of the EU through the DTS15/00153 research project. The authors also receive financial support from the ERDF and ESF of the EU, and Xunta de Galicia through the Centro Singular de Investigacin de Galicia, accreditation 2016–2019, ref. ED431G/01 and Grupos de Referencia Competitiva, ref. ED481A-2017/328 research project.
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Sutradhar, S., Rouco, J., Ortega, M. (2020). Unsupervised Anomaly Map for Image-Based Screening. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2019. EUROCAST 2019. Lecture Notes in Computer Science(), vol 12014. Springer, Cham. https://doi.org/10.1007/978-3-030-45096-0_30
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