AnoFPDM: Anomaly Detection with Forward Process of Diffusion Models for BrainMRI

Weakly-supervised diffusion models in anomaly segmentation, which leverage image-level labels and bypass the need for pixel-level labels during training, have shown superior performance over unsupervised methods, offering a cost-effective alternative. Traditional methods that rely on iterative image reconstruction are not fully weakly-supervised due to their dependence on costly pixel-level la-bels for hyperparameter tuning in inference. To address this issue, we introduce Anomaly Detection with Forward Process of Diffusion Models (AnoFPDM), a fully weakly-supervised framework that operates without image reconstruction and eliminates the need for pixel-level labels in hyperparameter tuning. By using the unguided forward process as a reference, AnoFPDM dynamically selects hyper-parameters such as noise scale and segmentation threshold for each input. We improve anomaly segmentation by ag-gregating anomaly maps from each step of the guided for-ward process, which strengthens the signal of anomalous regions in the aggregated anomaly map. Our framework demonstrates competitive performance on the BraTS21 and ATLAS v2.0 datasets. Code is available at https://github.com/SoloChe/AnoFPDM.