用CLIP模型零样本检测脑肿瘤，效果尚不满足临床需求。
Exploring Zero-Shot Anomaly Detection with CLIP in Medical Imaging: Are We There Yet?
- 用预训练CLIP模型直接检测医学图像异常，无需标注数据
- 在BraTS-MET数据集上准确率不足临床应用标准
- 适合关注跨领域迁移与医疗少样本学习的研究者
零样本异常检测（ZSAD）为无需特定任务训练即可识别医学影像异常提供了可能。本文评估了原本用于工业任务的基于CLIP的模型，在使用BraTS-MET数据集进行脑肿瘤检测时的表现。分析重点考察其在无监督或弱监督条件下检测医学特异性异常的能力，应对标注数据稀缺的挑战。尽管这些模型在将通用知识迁移到医学任务方面展现出潜力，但其性能尚未达到临床应用所需的精度。研究结果凸显了在实际应用于医学异常检测前，仍需进一步改进和适配。
原文摘要 · Abstract (English)
Zero-shot anomaly detection (ZSAD) offers potential for identifying anomalies in medical imaging without task-specific training. In this paper, we evaluate CLIP-based models, originally developed for industrial tasks, on brain tumor detection using the BraTS-MET dataset. Our analysis examines their ability to detect medical-specific anomalies with no or minimal supervision, addressing the challenges posed by limited data annotation. While these models show promise in transferring general knowledge to medical tasks, their performance falls short of the precision required for clinical use. Our findings highlight the need for further adaptation before CLIP-based models can be reliably applied to medical anomaly detection.
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