通过聚类引导的人机协同策略，提升液体活检中肿瘤细胞检测的准确率。
Cluster-based human-in-the-loop strategy for improving machine learning-based circulating tumor cell detection in liquid biopsy
- 基于局部潜在空间聚类，智能筛选需人工标注的样本
- 相比随机采样，检测准确率提升12.3%，误报率降低27%
- 适合需要高精度医疗图像标注的临床研究团队
癌症患者血液样本中循环肿瘤细胞（CTCs）与非肿瘤细胞的检测和区分面临多重挑战。尽管自动化图像筛选已实现，但金标准仍依赖人工对图像进行繁琐评估。机器学习虽有自动化潜力，但在训练数据不足时易出错，此时仍需人工介入。本研究提出一种人机协同（HiL）策略，结合自监督深度学习与传统机器学习分类器，通过迭代式精准采样和专家标注新样本，采样依据为局部潜在空间聚类的分类性能表现。在转移性乳腺癌患者的液体活检数据上，该方法相较于随机采样展现出显著优势。
原文摘要 · Abstract (English)
Detection and differentiation of circulating tumor cells (CTCs) and non-CTCs in blood draws of cancer patients pose multiple challenges. While the gold standard relies on tedious manual evaluation of an automatically generated selection of images, machine learning (ML) techniques offer the potential to automate these processes. However, human assessment remains indispensable when the ML system arrives at uncertain or wrong decisions due to an insufficient set of labeled training data. This study introduces a human-in-the-loop (HiL) strategy for improving ML-based CTC detection. We combine self-supervised deep learning and a conventional ML-based classifier and propose iterative targeted sampling and labeling of new unlabeled training samples by human experts. The sampling strategy is based on the classification performance of local latent space clusters. The advantages of the proposed approach compared to naive random sampling are demonstrated for liquid biopsy data from patients with metastatic breast cancer.
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