Dissolving Is Amplifying: Towards Fine-Grained Anomaly Detection

Medical imaging often contains critical fine-grained features, such as tumors or hemorrhages, crucial for diagnosis yet potentially too subtle for detection with conventional methods. In this paper, we introduce $\mathit{\text{DIA}}$, dissolving is amplifying. DIA is a fine-grained anomaly detection framework for medical images. First, we introduce $\mathit{\text{dissolving}}\mathit{\text{transformations}}$. We employ diffusion with a generative diffusion model as a dedicated feature-aware denoiser. Applying diffusion to medical images in a certain manner can remove or diminish fine-grained discriminative features. Second, we introduce an $\mathit{\text{amplifying}}\mathit{\text{framework}}$ based on contrastive learning to learn a semantically meaningful representation of medical images in a self-supervised manner, with a focus on fine-grained features. The amplifying framework contrasts additional pairs of images with and without dissolving transformations applied and thereby emphasizes the dissolved fine-grained features. DIA significantly improves the medical anomaly detection performance with around 18.40% AUC boost against the baseline method and achieves an overall SOTA against other benchmark methods. Our code is available at \url{https://github.com/shijianjian/DIA.git}.