Mask, Stitch, and Re-Sample: Enhancing Robustness and Generalizability
  in Anomaly Detection through Automatic Diffusion Models
Cosmin I. Bercea Michael Neumayr Daniel Rueckert Julia A. Schnabel
The introduction of diffusion models in anomaly detection has paved the way
for more effective and accurate image reconstruction in pathologies. However,
the current limitations in controlling noise granularity hinder diffusion
models' ability to generalize across diverse anomaly types and compromise the
restoration of healthy tissues. To overcome these challenges, we propose
AutoDDPM, a novel approach that enhances the robustness of diffusion models.
AutoDDPM utilizes diffusion models to generate initial likelihood maps of
potential anomalies and seamlessly integrates them with the original image.
Through joint noised distribution re-sampling, AutoDDPM achieves harmonization
and in-painting effects. Our study demonstrates the efficacy of AutoDDPM in
replacing anomalous regions while preserving healthy tissues, considerably
surpassing diffusion models' limitations. It also contributes valuable insights
and analysis on the limitations of current diffusion models, promoting robust
and interpretable anomaly detection in medical imaging - an essential aspect of
building autonomous clinical decision systems with higher interpretability.