  In the field of medical decision-making, precise anomaly detection in medical
imaging plays a pivotal role in aiding clinicians. However, previous work is
reliant on large-scale datasets for training anomaly detection models, which
increases the development cost. This paper first focuses on the task of medical
image anomaly detection in the few-shot setting, which is critically
significant for the medical field where data collection and annotation are both
very expensive. We propose an innovative approach, MediCLIP, which adapts the
CLIP model to few-shot medical image anomaly detection through self-supervised
fine-tuning. Although CLIP, as a vision-language model, demonstrates
outstanding zero-/fewshot performance on various downstream tasks, it still
falls short in the anomaly detection of medical images. To address this, we
design a series of medical image anomaly synthesis tasks to simulate common
disease patterns in medical imaging, transferring the powerful generalization
capabilities of CLIP to the task of medical image anomaly detection. When only
few-shot normal medical images are provided, MediCLIP achieves state-of-the-art
performance in anomaly detection and location compared to other methods.
Extensive experiments on three distinct medical anomaly detection tasks have
demonstrated the superiority of our approach. The code is available at
https://github.com/cnulab/MediCLIP.
