CVAD: A generic medical anomaly detector based on Cascade VAE
2021
Detecting out-of-distribution (OOD) samples in medical imaging plays an
important role for downstream medical diagnosis. However, existing OOD
detectors are demonstrated on natural images composed of inter-classes and have
difficulty generalizing to medical images. The key issue is the granularity of
OOD data in the medical domain, where intra-class OOD samples are predominant.
We focus on the generalizability of OOD detection for medical images and
propose a self-supervised Cascade Variational autoencoder-based Anomaly
Detector (CVAD). We use a variational autoencoders' cascade architecture, which
combines latent representation at multiple scales, before being fed to a
discriminator to distinguish the OOD data from the in-distribution (ID) data.
Finally, both the reconstruction error and the OOD probability predicted by the
binary discriminator are used to determine the anomalies. We compare the
performance with the state-of-the-art deep learning models to demonstrate our
model's efficacy on various open-access medical imaging datasets for both
intra- and inter-class OOD. Further extensive results on datasets including
common natural datasets show our model's effectiveness and generalizability.
The code is available at https://github.com/XiaoyuanGuo/CVAD.
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