Deep Frequency Re-calibration U-Net for Medical Image Segmentation

IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), pp. 3267–3276

Abstract

The human visual cortex is biased towards shape components while CNNs produce texture biased features. This fact may explain why the performance of CNN significantly degrades with low-labeled input data scenarios. In this paper, we propose a frequency re-calibration U-Net (FRCU-Net) for medical image segmentation. Representing an object in terms of frequency may reduce the effect of texture bias, resulting in better generalization for a low data regime. To do so, we apply the Laplacian pyramid in the bottleneck layer of the U-shaped structure. The Laplacian pyramid represents the object proposal in different frequency domains, where the high frequencies are responsible for the texture information and lower frequencies might be related to the shape. Adaptively re-calibrating these frequency representations can produce a more discriminative representation for describing the object of interest. To this end, we first propose to use a channel-wise attention mechanism to capture the relationship between the channels of a set of feature maps in one layer of the frequency pyramid. Second, the extracted features of each level of the pyramid are then combined through a non-linear function based on their impact on the final segmentation output. The proposed FRCU-Net is evaluated on five datasets ISIC 2017, ISIC 2018, the PH2, lung segmentation, and SegPC 2021 challenge datasets and compared to existing alternatives, achieving state-of-the-art results.

Authors 5

1. Reza Azad Aachen Institute of Imaging & Computer Vision

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Institute of Imaging and Computer Vision,Germany

   Institute of Imaging and Computer Vision, RWTH Aachen University, Germany

2. I Afshin Bozorgpour

   Sharif University of Technology

   Affiliation as printed

   Sharif University of Technology,Iran

   Sharif University of Technology, Iran

3. Maryam Asadi-Aghbolaghi

   Institute for Research in Fundamental Sciences

   Affiliation as printed

   Inst. for Research in Fundamental Sciences (IPM),School of Computer Science,Iran

   School of Computer Science, Inst. for Research in Fundamental Sciences (IPM), Iran

4. Dorit Merhof Aachen Institute of Imaging & Computer Vision

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Institute of Imaging and Computer Vision,Germany

   Institute of Imaging and Computer Vision, RWTH Aachen University, Germany

5. Sérgio Escalera

   Computer Vision Center · Universitat de Barcelona

   Affiliation as printed

   Universitat de Barcelona and Computer Vision Center,Spain

   Universitat de Barcelona and Computer Vision Center, Spain