ArticlePatterns (New York, N.Y.)2025
Cluster-based human-in-the-loop strategy for improving machine learning-based circulating tumor cell detection in liquid biopsy.
Article in Patterns (New York, N.Y.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Decoding the cancer microbiome: multi-omics, AI, and translational opportunities.Genome biology · 2026Review
- Artificial intelligence in oncology: linking biological discovery to clinical utility.Molecular cancer · 2026Review
- A scoping review of human-AI collaboration patterns and task divisions in healthcare applications.NPJ digital medicine · 2026Article
- Fast enrichment and detection of circulating tumor cells from large volumes of whole blood of breast cancer patients utilizing a functionalized bioaffinity CTC filtration membrane.International journal of cancer · 2026Article
- New horizons at the interface of artificial intelligence and translational cancer research.Cancer cell · 2025Review
- Review
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In liquid biopsy, detecting and differentiating circulating tumor cells (CTCs) and non-CTCs in metastatic cancer patients' blood samples remains challenging. The current gold standard often involves tedious manual examination of extensive image galleries. While machine learning (ML) offers potential automation, human expertise remains essential, particularly when ML systems face uncertainty or incorrect predictions due to limited labeled data. Combining self-supervised deep learning with an easily adaptable conventional ML classifier, we propose a human-in-the-loop approach with a targeted sampling strategy. By directing human efforts to label a limited set of new training samples from high-uncertainty clusters in the latent space, we iteratively reduce the system's uncertainty and improve classification performance, thereby saving time compared to naive sampling approaches. On data from metastatic breast cancer patients, we show the feasibility of our approach and achieve better performance while reducing expert evaluation time compared to the gold standard, the FDA-approved CellSearch system.
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Registered trials
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