Unsupervised Anomaly Detection in Medical Images Using Masked Diffusion Model
This work addresses the challenge of identifying anomalies in medical images without pixel-level labels, which is crucial for clinical applications where labeled data is scarce, though it is incremental as it builds on existing diffusion models with novel masking techniques.
The paper tackles the problem of unsupervised anomaly detection in brain MRI images by proposing a masked diffusion model (mDPPM) that uses masking-based regularization to generate anatomically consistent representations of healthy brains, achieving superior performance compared to existing supervised baselines on datasets with tumors and multiple sclerosis lesions.
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 masked-DDPM (mDPPM), 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 DPPM models 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. Code is available at https://github.com/hasan1292/mDDPM.