Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Images

Daniel Borders
Daniel Borders
Gregory N. McKay
Gregory N. McKay
Kevan J. Salimian
Kevan J. Salimian

IEEE transactions on medical imaging, Volume abs/1810.00236, 2020.

Cited by: 39|Views21
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Abstract:

Nuclei segmentation is a fundamental task for various computational pathology applications, including nuclei morphology analysis, cell type classification, and cancer grading. Deep learning has emerged as a powerful approach to segmenting nuclei, but the accuracy of convolutional neural networks (CNNs) depends on the volume and quality of...More

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