  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.
