Unsupervised Anomaly Detection for X-Ray Images
- URL: http://arxiv.org/abs/2001.10883v2
- Date: Wed, 4 Nov 2020 12:26:37 GMT
- Title: Unsupervised Anomaly Detection for X-Ray Images
- Authors: Diana Davletshina, Valentyn Melnychuk, Viet Tran, Hitansh Singla, Max
Berrendorf, Evgeniy Faerman, Michael Fromm, and Matthias Schubert
- Abstract summary: We investigate how unsupervised methods trained on images without anomalies can be used to assist doctors in evaluating X-ray images of hands.
We adopt state-of-the-art approaches for unsupervised learning to detect anomalies and show how the outputs of these methods can be explained.
We provide an extensive evaluation of different approaches and demonstrate empirically that even without labels it is possible to achieve satisfying results on a real-world dataset of X-ray images of hands.
- Score: 4.353258086186526
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Obtaining labels for medical (image) data requires scarce and expensive
experts. Moreover, due to ambiguous symptoms, single images rarely suffice to
correctly diagnose a medical condition. Instead, it often requires to take
additional background information such as the patient's medical history or test
results into account. Hence, instead of focusing on uninterpretable black-box
systems delivering an uncertain final diagnosis in an end-to-end-fashion, we
investigate how unsupervised methods trained on images without anomalies can be
used to assist doctors in evaluating X-ray images of hands. Our method
increases the efficiency of making a diagnosis and reduces the risk of missing
important regions. Therefore, we adopt state-of-the-art approaches for
unsupervised learning to detect anomalies and show how the outputs of these
methods can be explained. To reduce the effect of noise, which often can be
mistaken for an anomaly, we introduce a powerful preprocessing pipeline. We
provide an extensive evaluation of different approaches and demonstrate
empirically that even without labels it is possible to achieve satisfying
results on a real-world dataset of X-ray images of hands. We also evaluate the
importance of preprocessing and one of our main findings is that without it,
most of our approaches perform not better than random. To foster
reproducibility and accelerate research we make our code publicly available at
https://github.com/Valentyn1997/xray
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