Mask, Stitch, and Re-Sample: Enhancing Robustness and Generalizability in Anomaly Detection through Automatic Diffusion Models

• URL: http://arxiv.org/abs/2305.19643v1
• Date: Wed, 31 May 2023 08:21:17 GMT
• Title: Mask, Stitch, and Re-Sample: Enhancing Robustness and Generalizability in Anomaly Detection through Automatic Diffusion Models
• Authors: Cosmin I. Bercea and Michael Neumayr and Daniel Rueckert and Julia A. Schnabel
• Abstract summary: We propose AutoDDPM, a novel approach that enhances the robustness of diffusion models. Through joint noised distribution re-sampling, AutoDDPM achieves the harmonization and in-painting effects. It also contributes valuable insights and analysis on the limitations of current diffusion models.
• Score: 8.540959938042352
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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