Guo, XiaoyuanGichoya, Judy WawiraPurkayastha, SaptarshiBanerjee, Imon2022-10-052022-10-052021Guo, X., Gichoya, J. W., Purkayastha, S., & Banerjee, I. (2021). CVAD: A generic medical anomaly detector based on Cascade VAE. arXiv preprint arXiv:2110.15811.https://hdl.handle.net/1805/30204Detecting 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.enAttribution-NonCommercial-ShareAlike 4.0 InternationalCascade Variational autoencoderbased Anomaly DetectorCVADout-of-distributionArticle