SD-MAD: Sign-Driven Few-shot Multi-Anomaly Detection in Medical Images
Journal:
arXiv
Published Date:
May 22, 2025
Abstract
Medical anomaly detection (AD) is crucial for early clinical intervention,
yet it faces challenges due to limited access to high-quality medical imaging
data, caused by privacy concerns and data silos. Few-shot learning has emerged
as a promising approach to alleviate these limitations by leveraging the
large-scale prior knowledge embedded in vision-language models (VLMs). Recent
advancements in few-shot medical AD have treated normal and abnormal cases as a
one-class classification problem, often overlooking the distinction among
multiple anomaly categories. Thus, in this paper, we propose a framework
tailored for few-shot medical anomaly detection in the scenario where the
identification of multiple anomaly categories is required. To capture the
detailed radiological signs of medical anomaly categories, our framework
incorporates diverse textual descriptions for each category generated by a
Large-Language model, under the assumption that different anomalies in medical
images may share common radiological signs in each category. Specifically, we
introduce SD-MAD, a two-stage Sign-Driven few-shot Multi-Anomaly Detection
framework: (i) Radiological signs are aligned with anomaly categories by
amplifying inter-anomaly discrepancy; (ii) Aligned signs are selected further
to mitigate the effect of the under-fitting and uncertain-sample issue caused
by limited medical data, employing an automatic sign selection strategy at
inference. Moreover, we propose three protocols to comprehensively quantify the
performance of multi-anomaly detection. Extensive experiments illustrate the
effectiveness of our method.
