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Author

Kazue Yoneda

2 papers indexed here

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Open access Sep 2026

A plasma metabolomics workflow for breast cancer detection using quantitative GC/MS and machine learning

Blood-based metabolomic profiling has been widely investigated for breast cancer (BC) detection; however, clinical implementation remains limited due to variability in sample handling, analytical reproducibility, and overfitting during statistical analysis. We established a plasma GC/MS metabolomics workflow for discri...

Noriyuki Ojima, Tomonori Yokoyama, Shin Nishiumi et al. · 0 citations
Conference Aug 2026

Adaptation Depth Over Synthetic Augmentation for Classification of Imbalanced Circulating Tumor Cells

Circulating tumor cells (CTCs) are a non-invasive biomarker of metastasis. However, they are extremely rare, making automated detection in fluorescence microscopy images a severely class-imbalanced learning problem with a small, fixed-size positive class. A common approach is to synthesize additional minority examples...

Kouki Tsuji, Kazue Yoneda, Tohru Kamiya · 0 citations

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