MIDL 2021

International Conference on
Medical Imaging with Deep Learning
5. bis 7. Juli 2021

Deep Learning-based Generative Models for Unsupervised Anomaly Detection in Medical Images

Deep learning methods for image analysis have shown to excel at many tasks such as segmentation, detection and classification. However, they usually require a large amount of annotated images to learn a particular task. For example, if the aim is to segment brain tumors in MRI images, a deep learning algorithm would require hundreds or thousands of brain MRIs with expert annotations of the tumor tissue. Such datasets are rarely accessible in the medical field since the annotation of images is a complicated and time consuming process.

In this project, the focus lies on detecting pathologies and anomalies in medical images without using ground truth segmentations for training, so called unsupervised learning. The main idea is to learn and model the variability of healthy tissue appearance of a certain data domain, so that pathologies are recognized as deviations from the learned norm. Deep learning-based generative models such as variational autoencoders or GANs are a tool of choice in this project, since they enable the learning of complex representations and distributions.


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Fig.1: Example pathological images (left) and unsupervised localization of the pathologies (right) shown as heat maps. From up to down: Thorax CT with a large tumor; Brain MRI with contrasted glioblastoma tomor tissue; Retinal OCT with macular edema.

Selected Publications:

  1. Uzunova H., Handels H., Ehrhardt J.
    Unsupervised Pathology Detection in Medical Images using Learning-based Methods
    In: Maier A., Deserno T.M., Handels H., Maier-Hein K.H., Palm C., Tolxdorff T. (eds.), Bildverarbeitung für die Medizin 2018, Erlangen, Informatik aktuell, Springer Vieweg, Berlin Heidelberg, 61-66, 2018
  2. Uzunova H., Ehrhardt J., Kepp T., Handels H.
    Interpretable Explanations of Black Box Classifiers Applied on Medical Images by Meaningful Perturbations using Variational Autoencoders
    In: SPIE 10949, Medical Imaging 2019: Image Processing, San Diego, USA, 10949, 1094911-1-1094911-8, 2019


M.Sc. Hristina Uzunova
Dr. rer. nat. Jan Ehrhardt
Prof. Dr. rer. nat. habil. Heinz Handels


Created at May 9, 2019 - 1:11pm by Kulbe. Last modified at June 8, 2020 - 1:07pm by Kulbe.


Program of Study

Study Medical Informatics
at the University of Lübeck

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