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Electrical Engineering and Systems Science > Image and Video Processing

arXiv:2109.00957 (eess)
[Submitted on 1 Sep 2021 (v1), last revised 19 Oct 2021 (this version, v3)]

Title:Sk-Unet Model with Fourier Domain for Mitosis Detection

Authors: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.
Comments: Win 1st place in the MICCAI2021 MIDOG Challenge
Subjects: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2109.00957 [eess.IV]
  (or arXiv:2109.00957v3 [eess.IV] for this version)
  https://doi.org/10.48550/arXiv.2109.00957

Submission history

From: Sen Yang [view email]
[v1] Wed, 1 Sep 2021 17:10:39 UTC (95 KB)
[v2] Mon, 20 Sep 2021 13:55:18 UTC (6,700 KB)
[v3] Tue, 19 Oct 2021 13:44:08 UTC (6,700 KB)
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