Spectral U-Net: Enhancing Medical Image Segmentation via Spectral Decomposition
<|reference_start|>Spectral U-Net: Enhancing Medical Image Segmentation via Spectral Decomposition: This paper introduces Spectral U-Net, a novel deep learning network based on spectral decomposition, by exploiting Dual Tree Complex Wavelet Transform (DTCWT) for down-sampling and inverse Dual Tree Complex Wavelet Transform (iDTCWT) for up-sampling. We devise the corresponding Wave-Block and iWave-Block, integrated into the U-Net architecture, aiming at mitigating information loss during down-sampling and enhancing detail reconstruction during up-sampling. In the encoder, we first decompose the feature map into high and low-frequency components using DTCWT, enabling down-sampling while mitigating information loss. In the decoder, we utilize iDTCWT to reconstruct higher-resolution feature maps from down-sampled features. Evaluations on the Retina Fluid, Brain Tumor, and Liver Tumor segmentation datasets with the nnU-Net framework demonstrate the superiority of the proposed Spectral U-Net.<|reference_end|>
@article{peng2024spectral,
    title={Spectral U-Net: Enhancing Medical Image Segmentation via Spectral
  Decomposition},
    author={Yaopeng Peng, Milan Sonka, Danny Z. Chen},
    journal={arXiv preprint arXiv:2409.09216},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.09216},
    primaryClass={eess.IV cs.CV}
}