FFCNet: Fourier Transform-Based Frequency Learning and Complex Convolutional Network for Colon Disease Classification

• URL: http://arxiv.org/abs/2207.01287v1
• Date: Mon, 4 Jul 2022 09:32:23 GMT
• Title: FFCNet: Fourier Transform-Based Frequency Learning and Complex Convolutional Network for Colon Disease Classification
• Authors: Kai-Ni Wang, Yuting He, Shuaishuai Zhuang, Juzheng Miao, Xiaopu He, Ping Zhou, Guanyu Yang, Guang-Quan Zhou, Shuo Li
• Abstract summary: We propose a Fourier-based Frequency Complex Network (FFCNet) for colon disease classification. FFCNet is a novel complex network that enables the combination of complex convolutional networks with frequency learning. Our method achieves high performance outperforming previous state-of-the art methods with an accuracy of 86:35% and an accuracy of 4.46% higher than the backbone.
• Score: 6.483399675485918
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reliable automatic classification of colonoscopy images is of great significance in assessing the stage of colonic lesions and formulating appropriate treatment plans. However, it is challenging due to uneven brightness, location variability, inter-class similarity, and intra-class dissimilarity, affecting the classification accuracy. To address the above issues, we propose a Fourier-based Frequency Complex Network (FFCNet) for colon disease classification in this study. Specifically, FFCNet is a novel complex network that enables the combination of complex convolutional networks with frequency learning to overcome the loss of phase information caused by real convolution operations. Also, our Fourier transform transfers the average brightness of an image to a point in the spectrum (the DC component), alleviating the effects of uneven brightness by decoupling image content and brightness. Moreover, the image patch scrambling module in FFCNet generates random local spectral blocks, empowering the network to learn long-range and local diseasespecific features and improving the discriminative ability of hard samples. We evaluated the proposed FFCNet on an in-house dataset with 2568 colonoscopy images, showing our method achieves high performance outperforming previous state-of-the art methods with an accuracy of 86:35% and an accuracy of 4.46% higher than the backbone. The project page with code is available at https://github.com/soleilssss/FFCNet.

Related papers

• Freqformer: Image-Demoiréing Transformer via Efficient Frequency Decomposition [83.40450475728792]
  We present Freqformer, a Transformer-based framework specifically designed for image demoir'eing through targeted frequency separation.<n>Our method performs an effective frequency decomposition that explicitly splits moir'e patterns into high-frequency spatially-localized textures and low-frequency scale-robust color distortions.<n>Experiments on various demoir'eing benchmarks demonstrate that Freqformer achieves state-of-the-art performance with a compact model size.
  arXiv  Detail & Related papers  (2025-05-25T12:23:10Z)
• FreqU-FNet: Frequency-Aware U-Net for Imbalanced Medical Image Segmentation [0.0]
  FreqU-FNet is a novel U-shaped segmentation architecture operating in the frequency domain.<n>Our framework incorporates a Frequency that leverages Low-Pass Convolution and Daubechies wavelet-based downsampling.<n>Experiments on multiple medical segmentation benchmarks demonstrate that FreqU-FNet consistently outperforms both CNN and Transformer baselines.
  arXiv  Detail & Related papers  (2025-05-23T06:51:24Z)
• How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings [106.3726679697804]
  We compare the two most common techniques for mitigating this spectral bias: Fourier feature encodings (FFE) and multigrid parametric encodings (MPE) MPEs are seen as the standard for low dimensional mappings, but MPEs often outperform them and learn representations with higher resolution and finer detail. We prove that MPEs improve a network's performance through the structure of their grid and not their learnable embedding.
  arXiv  Detail & Related papers  (2025-04-18T02:18:08Z)
• Self-Bootstrapping for Versatile Test-Time Adaptation [29.616417768209114]
  We develop a versatile test-time adaptation (TTA) objective for a variety of tasks. We achieve this through a self-bootstrapping scheme that optimize prediction consistency between the test image (as target) and its deteriorated view. Experiments show that, either independently or as a plug-and-play module, our method achieves superior results across classification, segmentation, and 3D monocular detection tasks.
  arXiv  Detail & Related papers  (2025-04-10T05:45:07Z)
• Misalignment-Robust Frequency Distribution Loss for Image Transformation [51.0462138717502]
  This paper aims to address a common challenge in deep learning-based image transformation methods, such as image enhancement and super-resolution. We introduce a novel and simple Frequency Distribution Loss (FDL) for computing distribution distance within the frequency domain. Our method is empirically proven effective as a training constraint due to the thoughtful utilization of global information in the frequency domain.
  arXiv  Detail & Related papers  (2024-02-28T09:27:41Z)
• Affine-Consistent Transformer for Multi-Class Cell Nuclei Detection [76.11864242047074]
  We propose a novel Affine-Consistent Transformer (AC-Former), which directly yields a sequence of nucleus positions. We introduce an Adaptive Affine Transformer (AAT) module, which can automatically learn the key spatial transformations to warp original images for local network training. Experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art algorithms on various benchmarks.
  arXiv  Detail & Related papers  (2023-10-22T02:27:02Z)
• PRISTA-Net: Deep Iterative Shrinkage Thresholding Network for Coded Diffraction Patterns Phase Retrieval [6.982256124089]
  Phase retrieval is a challenge nonlinear inverse problem in computational imaging and image processing. We have developed PRISTA-Net, a deep unfolding network based on the first-order iterative threshold threshold algorithm (ISTA) All parameters in the proposed PRISTA-Net framework, including the nonlinear transformation, threshold, and step size, are learned-to-end instead of being set.
  arXiv  Detail & Related papers  (2023-09-08T07:37:15Z)
• Unlocking Fine-Grained Details with Wavelet-based High-Frequency Enhancement in Transformers [4.208461204572879]
  Medical image segmentation is a critical task that plays a vital role in diagnosis, treatment planning, and disease monitoring. We address the local feature deficiency of the Transformer model by carefully re-designing the self-attention map. We propose a multi-scale context enhancement block within skip connections to adaptively model inter-scale dependencies.
  arXiv  Detail & Related papers  (2023-08-25T15:42:19Z)
• Performance of GAN-based augmentation for deep learning COVID-19 image classification [57.1795052451257]
  The biggest challenge in the application of deep learning to the medical domain is the availability of training data. Data augmentation is a typical methodology used in machine learning when confronted with a limited data set. In this work, a StyleGAN2-ADA model of Generative Adversarial Networks is trained on the limited COVID-19 chest X-ray image set.
  arXiv  Detail & Related papers  (2023-04-18T15:39:58Z)
• Increasing the Accuracy of a Neural Network Using Frequency Selective Mesh-to-Grid Resampling [4.211128681972148]
  We propose the use of keypoint frequency selective mesh-to-grid resampling (FSMR) for the processing of input data for neural networks. We show that depending on the network architecture and classification task the application of FSMR during training aids learning process. The classification accuracy can be increased by up to 4.31 percentage points for ResNet50 and the Oxflower17 dataset.
  arXiv  Detail & Related papers  (2022-09-28T21:34:47Z)
• Preservation of High Frequency Content for Deep Learning-Based Medical Image Classification [74.84221280249876]
  An efficient analysis of large amounts of chest radiographs can aid physicians and radiologists. We propose a novel Discrete Wavelet Transform (DWT)-based method for the efficient identification and encoding of visual information.
  arXiv  Detail & Related papers  (2022-05-08T15:29:54Z)
• SpectralFormer: Rethinking Hyperspectral Image Classification with Transformers [91.09957836250209]
  Hyperspectral (HS) images are characterized by approximately contiguous spectral information. CNNs have been proven to be a powerful feature extractor in HS image classification. We propose a novel backbone network called ulSpectralFormer for HS image classification.
  arXiv  Detail & Related papers  (2021-07-07T02:59:21Z)
• Global Filter Networks for Image Classification [90.81352483076323]
  We present a conceptually simple yet computationally efficient architecture that learns long-term spatial dependencies in the frequency domain with log-linear complexity. Our results demonstrate that GFNet can be a very competitive alternative to transformer-style models and CNNs in efficiency, generalization ability and robustness.
  arXiv  Detail & Related papers  (2021-07-01T17:58:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.