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
this https URL

在医学决策领域，精确的医学成像异常检测在帮助临床医生方面起着关键作用。本文首先侧重于医学图像异常检测在少样本情景下的任务，这对于数据采集和注释都非常昂贵的医学领域至关重要。我们提出了一种创新的方法，MediCLIP，通过自监督微调，将 CLIP 模型适应于少样本医学图像异常检测。当仅提供少量正常医学图像时，MediCLIP 相比其他方法在异常检测和定位方面实现了最先进的性能。三个不同的医学异常检测任务上的大量实验证明了我们方法的优越性。