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Unsupervised Anomaly Detection on Histopathology Images Using Adversarial Learning and Simulated Anomaly

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Medical Image Understanding and Analysis (MIUA 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14859))

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Abstract

Automated analytics in computational histopathology have shown significant progress in aiding pathologists through digital image analysis. However, developing a robust model based on supervised learning for histopathology images is challenging because of the scarcity of tumor-marked samples and unknown diseases. Unsupervised anomaly detection (UAD) methods that were mostly used in industrial inspection are thus proposed to facilitate efficient analytics. UAD only requires normal samples for training and largely reduces the burden of labeling. In this paper, we introduce a reconstruction-based UAD approach called PathUAD to improve representation learning based on adversarial learning and simulated anomalies. On the one hand, we mix up features extracted from normal images to build a smoother feature distribution and employ adversarial learning to enhance an autoencoder for image reconstruction. On the other hand, we simulate anomalous images by image deformation, and guide the autoencoder to catch global characteristics of normal images well. We demonstrate its effectiveness on a histopathology anomaly detection benchmark and show state-of-the-art performance.

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Acknowledgement

This work was funded in part by Qualcomm through a Taiwan University Research Collaboration Project and in part by the National Science and Technology Council, Taiwan, under grants 113-2425-H-006-007, 112-2634-F-006-002, 112-2221-E-006-136-MY3, 112-2622-E-006-021, and 110-2221-E-006-127-MY3.

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Authors and Affiliations

  1. National Cheng Kung University, Tainan City, Taiwan

    Yu-Chen Lai & Wei-Ta Chu

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  1. Yu-Chen Lai
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  2. Wei-Ta Chu
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Correspondence to Yu-Chen Lai or Wei-Ta Chu .

Editor information

Editors and Affiliations

  1. Manchester Metropolitan University, Manchester, Lancashire, UK

    Moi Hoon Yap

  2. Manchester Metropolitan University, Manchester, UK

    Connah Kendrick

  3. Edge Hill University, Ormskirk, UK

    Ardhendu Behera

  4. Aberystwyth University, Aberystwyth, UK

    Timothy Cootes

  5. Aberystwyth University, Aberystwyth, UK

    Reyer Zwiggelaar

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Lai, YC., Chu, WT. (2024). Unsupervised Anomaly Detection on Histopathology Images Using Adversarial Learning and Simulated Anomaly. In: Yap, M.H., Kendrick, C., Behera, A., Cootes, T., Zwiggelaar, R. (eds) Medical Image Understanding and Analysis. MIUA 2024. Lecture Notes in Computer Science, vol 14859. Springer, Cham. https://doi.org/10.1007/978-3-031-66955-2_25

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  • DOI: https://doi.org/10.1007/978-3-031-66955-2_25

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  • Online ISBN: 978-3-031-66955-2

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