Dissolving Is Amplifying: Towards Fine-Grained Anomaly Detection
This work addresses the challenge of fine-grained anomaly detection in medical imaging, which is crucial for accurate diagnosis, representing a novel method for a known bottleneck.
The paper tackles the problem of detecting subtle anomalies like tumors in medical images by introducing DIA, a framework that uses dissolving transformations to remove fine-grained features and an amplifying framework to emphasize them, achieving an 18.40% AUC boost over the baseline and state-of-the-art performance.
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 \textit{DIA}, dissolving is amplifying. DIA is a fine-grained anomaly detection framework for medical images. First, we introduce \textit{dissolving 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 \textit{amplifying 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}.