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A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment
Published on: May 24, 2022
Yan Yang1, Phillip Stafford, YoonJoo Kim
1School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ 85287, USA. Yan.Yang@asu.edu
Signal saturation in microarray images biases gene expression data. A new mixture model method corrects this bias by adjusting segmentation, improving diagnostic accuracy in cancer studies.
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