Abstract
Unsupervised anomaly segmentation (UAS) is promising in many computer vision applications, e.g., the analysis of brain MRI, thanks to the advantage of detecting the anomalies (lesions) by only using the normal samples (healthy anatomies) in the training phase. Existing methods utilize the reconstruction process to model the normative distribution but inevitably lead to the impairment of localization information, which is critical for the pixel-level detection task. In this paper, we address this challenge by formulating a semantic layout of the healthy anatomy as the reconstruction manifold, which naturally forces the embedding to explicitly encode more semantic features as well as facilitates the preservation of spatial information during the reconstruction. Based on this special autoencoder framework of Semantic-Manifold Reconstruction (SMR), we further apply two consistency regularizations not only on the semantic layout but also the image appearance. In this way a Dual Semantic-Manifold Reconstruction (DSMR) is trained and then used to detect the anomalies accurately. Experiments reveal that the proposed DSMR approach exceeds the state-of-the-art performance on the benchmark datasets of BraTS and ISLES.
The study is supported partly by the National Natural Science Foundation of China under Grants 82172033, U19B2031, 52105126, 82272071, 62271430, and 61971369.
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Notes
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where Im denotes image and Se denotes semantic layout.
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Ding, Z., Dong, Q., Xu, H., Li, C., Ding, X., Huang, Y. (2023). Unsupervised Anomaly Segmentation for Brain Lesions Using Dual Semantic-Manifold Reconstruction. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Lecture Notes in Computer Science, vol 13625. Springer, Cham. https://doi.org/10.1007/978-3-031-30111-7_12
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