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This is a Quantitative Study study.

September 23, 2025Open Access

MicroNucML: A machine learning approach for micronuclei segmentation and the refinement of nuclei-micronuclei relationships

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This approach develops a machine learning model for micronuclei segmentation, highlighting genomic instability in DNA damage conditions.

The model achieved state-of-the-art performance in micronuclei detection, contributing to biological insights.
Data augmentation enhanced the model's ability to generalize across diverse image qualities and colors.
The use of a two-phase labeling approach refined the segmentation process for small DNA fragments.
Accessible resources for studying genomic instability with improved accuracy and sensitivity were established.

Wang et al. (2025) studied this question.

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