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Unsupervised Anomaly Detection in Medical Images Using Masked Diffusion Model

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Machine Learning in Medical Imaging (MLMI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14348))

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Abstract

It can be challenging to identify brain MRI anomalies using supervised deep-learning techniques due to anatomical heterogeneity and the requirement for pixel-level labeling. Unsupervised anomaly detection approaches provide an alternative solution by relying only on sample-level labels of healthy brains to generate a desired representation to identify abnormalities at the pixel level. Although, generative models are crucial for generating such anatomically consistent representations of healthy brains, accurately generating the intricate anatomy of the human brain remains a challenge. In this study, we present a method called the masked-denoising diffusion probabilistic model (mDDPM), which introduces masking-based regularization to reframe the generation task of diffusion models. Specifically, we introduce Masked Image Modeling (MIM) and Masked Frequency Modeling (MFM) in our self-supervised approach that enables models to learn visual representations from unlabeled data. To the best of our knowledge, this is the first attempt to apply MFM in denoising diffusion probabilistic models (DDPMs) for medical applications. We evaluate our approach on datasets containing tumors and numerous sclerosis lesions and exhibit the superior performance of our unsupervised method as compared to the existing fully/weakly supervised baselines. Project website: https://mddpm.github.io/.

H. Iqbal and U. Khalid—Equal Contribution.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Wayne State University, Detroit, MI, USA

    Hasan Iqbal & **g Hua

  2. Center for Research in Computer Vision, University of Central Florida, Orlando, FL, USA

    Umar Khalid & Chen Chen

Authors
  1. Hasan Iqbal
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  2. Umar Khalid
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  3. Chen Chen
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  4. **g Hua
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Corresponding author

Correspondence to Hasan Iqbal .

Editor information

Editors and Affiliations

  1. Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China

    **aohuan Cao

  2. Rensselaer Polytechnic Institute, Troy, NY, USA

    Xuanang Xu

  3. Imperial College London, London, UK

    Islem Rekik

  4. ShanghaiTech University, Shanghai, China

    Zhiming Cui

  5. Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China

    ** Ouyang

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Iqbal, H., Khalid, U., Chen, C., Hua, J. (2024). Unsupervised Anomaly Detection in Medical Images Using Masked Diffusion Model. In: Cao, X., Xu, X., Rekik, I., Cui, Z., Ouyang, X. (eds) Machine Learning in Medical Imaging. MLMI 2023. Lecture Notes in Computer Science, vol 14348. Springer, Cham. https://doi.org/10.1007/978-3-031-45673-2_37

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  • DOI: https://doi.org/10.1007/978-3-031-45673-2_37

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