Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
10-2023
Abstract
Unsupervised anomaly detection (UAD) aims to find anomalous images by optimising a detector using a training set that contains only normal images. UAD approaches can be based on reconstruction methods, self-supervised approaches, and Imagenet pre-trained models. Reconstruction methods, which detect anomalies from image reconstruction errors, are advantageous because they do not rely on the design of problem-specific pretext tasks needed by self-supervised approaches, and on the unreliable translation of models pre-trained from non-medical datasets. However, reconstruction methods may fail because they can have low reconstruction errors even for anomalous images. In this paper, we introduce a new reconstruction-based UAD approach that addresses this low-reconstruction error issue for anomalous images. Our UAD approach, the memory-augmented multi-level cross-attentional masked autoencoder (MemMC-MAE), is a transformer-based approach, consisting of a novel memory-augmented self-attention operator for the encoder and a new multi-level cross-attention operator for the decoder. MemMC-MAE masks large parts of the input image during its reconstruction, reducing the risk that it will produce low reconstruction errors because anomalies are likely to be masked and cannot be reconstructed. However, when the anomaly is not masked, then the normal patterns stored in the encoder's memory combined with the decoder's multi-level cross-attention will constrain the accurate reconstruction of the anomaly. We show that our method achieves SOTA anomaly detection and localisation on colonoscopy, pneumonia, and covid-19 chest x-ray datasets.
Keywords
Pneumonia, Covid-19, Colonoscopy, Unsupervised Learning, Anomaly Detection' Anomaly Segmentation, Vision Transformer
Discipline
Artificial Intelligence and Robotics | Graphics and Human Computer Interfaces
Research Areas
Intelligent Systems and Optimization
Publication
Machine Learning in Medical Imaging: 14th International Workshop, MLMI 2023, Vancouver, October 8: Proceedings
Volume
14349
First Page
11
Last Page
21
ISBN
9783031456763
Identifier
10.1007/978-3-031-45676-3_2
Publisher
Springer
City or Country
Cham
Citation
TIAN, Yu; PANG, Guansong; LIU, Yuyuan; WANG, Chong; CHEN, Yuanhong; LIU, Fengbei; SINGH, Rajvinder; VERJANS, Johan W.; WANG, Mengyu; and CARNEIRO, Gustavo.
Unsupervised anomaly detection in medical images with a memory-augmented multi-level cross-attentional masked autoencoder. (2023). Machine Learning in Medical Imaging: 14th International Workshop, MLMI 2023, Vancouver, October 8: Proceedings. 14349, 11-21.
Available at: https://ink.library.smu.edu.sg/sis_research/8329
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
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