Distributional drift detection is important in medical applications as it helps ensure the accuracy and reliability of models by identifying changes in the underlying data distribution that could affect diagnostic or treatment decisions. However, current methods have limitations in detecting drift; for example, the inclusion of abnormal datasets can lead to unfair comparisons. This paper presents an accurate and sensitive approach to detect distributional drift in CT-scan medical images by leveraging data-sketching and fine-tuning techniques. We developed a robust baseline library model for real-time anomaly detection, allowing for efficient comparison of incoming images and identification of anomalies. Additionally, we fine-tuned a vision transformer pre-trained model to extract relevant features using breast cancer images as an example, significantly enhancing model accuracy to 99.11\%. Combining with data-sketches and fine-tuning, our feature extraction evaluation demonstrated that cosine similarity scores between similar datasets provide greater improvements, from around 50\% increased to 100\%. Finally, the sensitivity evaluation shows that our solutions are highly sensitive to even 1\% salt-and-pepper and speckle noise, and it is not sensitive to lighting noise (e.g., lighting conditions have no impact on data drift). The proposed methods offer a scalable and reliable solution for maintaining the accuracy of diagnostic models in dynamic clinical environments.

翻译：分布漂移检测在医疗应用中至关重要，它通过识别可能影响诊断或治疗决策的底层数据分布变化，有助于确保模型的准确性与可靠性。然而，现有方法在检测漂移方面存在局限；例如，异常数据集的纳入可能导致不公平的比较。本文提出一种利用数据草图与微调技术来检测CT扫描医学影像中分布漂移的精确且灵敏的方法。我们开发了一个用于实时异常检测的鲁棒基线库模型，能够高效比较输入图像并识别异常。此外，我们以乳腺癌图像为例，对预训练的视觉Transformer模型进行微调以提取相关特征，将模型准确率显著提升至99.11%。结合数据草图与微调技术，我们的特征提取评估表明，相似数据集间的余弦相似度得分提供了更大的改进，从约50%提升至100%。最后，敏感性评估显示，我们的解决方案对低至1%的椒盐噪声与斑点噪声高度敏感，而对光照噪声不敏感（例如，光照条件对数据漂移无影响）。所提出的方法为在动态临床环境中维持诊断模型的准确性提供了可扩展且可靠的解决方案。