Diffusion Models for Medical Anomaly Detection
This work addresses the problem of detecting anomalies in medical images with only image-level annotations, offering a simpler and more detailed alternative to existing methods like GANs or autoencoders, though it is incremental in its approach.
The paper tackles weakly supervised anomaly detection in medical images by proposing a method based on denoising diffusion implicit models, which generates detailed anomaly maps without complex training and achieves competitive performance on datasets like BRATS2020 and CheXpert.
In medical applications, weakly supervised anomaly detection methods are of great interest, as only image-level annotations are required for training. Current anomaly detection methods mainly rely on generative adversarial networks or autoencoder models. Those models are often complicated to train or have difficulties to preserve fine details in the image. We present a novel weakly supervised anomaly detection method based on denoising diffusion implicit models. We combine the deterministic iterative noising and denoising scheme with classifier guidance for image-to-image translation between diseased and healthy subjects. Our method generates very detailed anomaly maps without the need for a complex training procedure. We evaluate our method on the BRATS2020 dataset for brain tumor detection and the CheXpert dataset for detecting pleural effusions.