CVAD: An Anomaly Detector for Medical Images Based on Cascade VAE

Xiaoyuan Guo, Judy Wawira Gichoya, Saptarshi Purkayastha, Imon Banerjee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Anomaly detection in medical imaging plays an important role to ensure AI generalization. However, existing out-of-distribution (OOD) detection approaches fail to account for OOD data granularity in medical images, where identifying both intra-class and inter-class OOD data is essential to the generalizability in the medical domain. We focus on the generalizability of outlier detection for medical images and propose a generic Cascade Variational autoencoder-based Anomaly Detector (CVAD). We use 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 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 natural and medical imaging datasets for intra- and inter-class OOD. Extensive experimental results on multiple datasets show our model’s effectiveness and generalizability. The code will be publicly available.

Original languageEnglish (US)
Title of host publicationMedical Image Learning with Limited and Noisy Data - 1st International Workshop, MILLanD 2022, Held in Conjunction with MICCAI 2022, Proceedings
EditorsGhada Zamzmi, Sameer Antani, Sivaramakrishnan Rajaraman, Zhiyun Xue, Ulas Bagci, Marius George Linguraru
PublisherSpringer Science and Business Media Deutschland GmbH
Pages187-196
Number of pages10
ISBN (Print)9783031167591
DOIs
StatePublished - 2022
Event1st International Workshop on Medical Image Learning with Limited and Noisy Data, MILLanD 2022, held in conjunction with 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022 - Singapore, Singapore
Duration: Sep 22 2022Sep 22 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13559 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st International Workshop on Medical Image Learning with Limited and Noisy Data, MILLanD 2022, held in conjunction with 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022
Country/TerritorySingapore
CitySingapore
Period9/22/229/22/22

Keywords

  • Anomaly detection
  • Cascade Variational autoencoder
  • Medical images
  • OOD detection

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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