ArticleSensors (Basel, Switzerland)2024
Improving Circulating Tumor Cell Detection Using Image Synthesis and Transformer Models in Cancer Diagnostics.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Functionalized Carbon Dots from Bio-Based Precursors as Promising Fluorescent Probes for Cancer Cell Imaging.International journal of molecular sciences · 2025Article
- Representation learning enables robust single cell phenotyping in whole slide liquid biopsy imaging.Scientific reports · 2025Article
- Diagnostic value of circulating tumor cells in gastric cancer: a systematic review and meta-analysis.Translational cancer research · 2025Article
- Contrastive Representation Learning for Single Cell Phenotyping in Whole Slide Imaging of Enrichment-free Liquid Biopsy.bioRxiv : the preprint server for biology · 2025Article
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Authors and funding
3 authors.
Funding
Abstract
Cancer is the second leading cause of death, significantly threatening human health. Effective treatment options are often lacking in advanced stages, making early diagnosis crucial for reducing mortality rates. Circulating tumor cells (CTCs) are a promising biomarker for early detection; however, their automatic detection is challenging due to their heterogeneous size and shape, as well as their scarcity in blood. This study proposes a data generation method using the Segment Anything Model (SAM) combined with a copy-paste strategy. We develop a detection network based on the Swin Transformer, featuring a backbone network, scale adapter module, shape adapter module, and detection head, which enhances CTC localization and identification in images. To effectively utilize both generated and real data, we introduce an improved loss function that includes a regularization term to ensure consistency across different data distributions. Our model demonstrates exceptional performance across five evaluation metrics: accuracy (0.9960), recall (0.9961), precision (0.9804), specificity (0.9975), and mean average precision (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.