探索在医学影像中使用CLIP的零样本异常检测：我们到了吗？
Exploring Zero-Shot Anomaly Detection with CLIP in Medical Imaging: Are We There Yet?
AI总结：
本文评估基于CLIP的模型在BraTS-MET数据集上的零样本脑肿瘤检测能力，发现其虽具迁移潜力但精度未达临床要求，需进一步适应改进。
摘要：
零样本异常检测（ZSAD）具有在无需特定任务训练的情况下识别医学影像中异常的潜力。本文评估了最初为工业任务开发的基于CLIP的模型，使用BraTS-MET数据集进行脑肿瘤检测。我们的分析考察了它们在无监督或最小监督下检测医学特定异常的能力，以应对数据标注有限带来的挑战。尽管这些模型在将通用知识迁移到医学任务方面显示出潜力，但其性能仍达不到临床应用所需的精度。我们的发现强调，在基于CLIP的模型能够可靠地应用于医学异常检测之前，仍需进一步的适应。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
