Dissolving Is Amplifying: Towards Fine-Grained Anomaly Detection

Jian Shi, Pengyi Zhang, Ni ZHANG, Hakim Ghazzai, Peter Wonka

Keywords: anomaly detection, fine-grained features, diffusion models

Abstract:

In this paper, we introduce \textit{DIA}, dissolving is amplifying. DIA is a fine-grained anomaly detection framework for medical images. We describe two novel components in the paper.
First, we introduce \textit{dissolving transformations}. Our main observation is that generative diffusion models are feature-aware and applying them to medical images in a certain manner can remove or diminish fine-grained discriminative features such as tumors or hemorrhaging.
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. The amplifying framework contrasts additional pairs of images with and without dissolving transformations applied and thereby boosts the learning of fine-grained feature representations.
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{http://}.

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

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Submission Number: 1802