Diffusion Models with Ensembled Structure-Based Anomaly Scoring for Unsupervised Anomaly Detection
This work addresses the challenge of rare disease detection in medical imaging without requiring annotated data, though it is incremental as it builds on existing diffusion models and SSIM methods.
The paper tackled the problem of unsupervised anomaly detection in medical images by proposing an adaptive ensembling strategy for Structural Similarity (SSIM) scoring to capture both intensity and structural differences, enhancing performance across varying pathologies in brain MRI.
Supervised deep learning techniques show promise in medical image analysis. However, they require comprehensive annotated data sets, which poses challenges, particularly for rare diseases. Consequently, unsupervised anomaly detection (UAD) emerges as a viable alternative for pathology segmentation, as only healthy data is required for training. However, recent UAD anomaly scoring functions often focus on intensity only and neglect structural differences, which impedes the segmentation performance. This work investigates the potential of Structural Similarity (SSIM) to bridge this gap. SSIM captures both intensity and structural disparities and can be advantageous over the classical $l1$ error. However, we show that there is more than one optimal kernel size for the SSIM calculation for different pathologies. Therefore, we investigate an adaptive ensembling strategy for various kernel sizes to offer a more pathology-agnostic scoring mechanism. We demonstrate that this ensembling strategy can enhance the performance of DMs and mitigate the sensitivity to different kernel sizes across varying pathologies, highlighting its promise for brain MRI anomaly detection.