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arXiv · 2109.00957

Sk-Unet Model with Fourier Domain for Mitosis Detection

Sen Yang·Feng Luo·Jun Zhang·Xiyue Wang
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Abstract

Mitotic count is the most important morphological feature of breast cancer grading. Many deep learning-based methods have been proposed but suffer from domain shift. In this work, we construct a Fourier-based segmentation model for mitosis detection to address the problem. Swapping the low-frequency spectrum of source and target images is shown effective to alleviate the discrepancy between different scanners. Our Fourier-based segmentation method can achieve F1 with 0.7456 on the preliminary test set.

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Sen Yang, Feng Luo, Jun Zhang, Xiyue Wang. 2021-10-19. Sk-Unet Model with Fourier Domain for Mitosis Detection. https://arxiv.org/abs/2109.00957

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