1 research outputs found
Unsupervised Anomaly Detection for X-Ray Images
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/xra