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.

翻译：调节计数是乳腺癌等级中最重要的形态特征。 已经提出了许多深层次的基于学习的方法,但都存在领域变化。 在这项工作中,我们建立了一个基于Fourier的分化模型,用于对肾上腺素进行检测,以解决这个问题。 交换低频源和目标图像被证明是有效的,可以缓解不同扫描仪之间的差异。 我们基于Fourier的分化方法可以在初步测试集上达到F1, 即0. 7456。