Masked Autoencoders for Low dose CT denoising

• URL: http://arxiv.org/abs/2210.04944v1
• Date: Mon, 10 Oct 2022 18:27:58 GMT
• Title: Masked Autoencoders for Low dose CT denoising
• Authors: Dayang Wang, Yongshun Xu, Shuo Han, Hengyong Yu
• Abstract summary: Masked autoencoders (MAE) have been proposed as an effective label-free self-pretraining method for transformers. We redesign the classical encoder-decoder learning model to match the denoising task and apply it to LDCT denoising problem.
• Score: 9.575051352192697
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Low-dose computed tomography (LDCT) reduces the X-ray radiation but compromises image quality with more noises and artifacts. A plethora of transformer models have been developed recently to improve LDCT image quality. However, the success of a transformer model relies on a large amount of paired noisy and clean data, which is often unavailable in clinical applications. In computer vision and natural language processing fields, masked autoencoders (MAE) have been proposed as an effective label-free self-pretraining method for transformers, due to its excellent feature representation ability. Here, we redesign the classical encoder-decoder learning model to match the denoising task and apply it to LDCT denoising problem. The MAE can leverage the unlabeled data and facilitate structural preservation for the LDCT denoising model when ground truth data are missing. Experiments on the Mayo dataset validate that the MAE can boost the transformer's denoising performance and relieve the dependence on the ground truth data.

Related papers

• DenoMamba: A fused state-space model for low-dose CT denoising [6.468495781611433]
  Low-dose computed tomography (LDCT) lower potential risks linked to radiation exposure. LDCT denoising is based on neural network models that learn data-driven image priors to separate noise evoked by dose reduction from underlying tissue signals. DenoMamba is a novel denoising method based on state-space modeling (SSM) that efficiently captures short- and long-range context in medical images.
  arXiv  Detail & Related papers  (2024-09-19T21:32:07Z)
• CathFlow: Self-Supervised Segmentation of Catheters in Interventional Ultrasound Using Optical Flow and Transformers [66.15847237150909]
  We introduce a self-supervised deep learning architecture to segment catheters in longitudinal ultrasound images. The network architecture builds upon AiAReSeg, a segmentation transformer built with the Attention in Attention mechanism. We validated our model on a test dataset, consisting of unseen synthetic data and images collected from silicon aorta phantoms.
  arXiv  Detail & Related papers  (2024-03-21T15:13:36Z)
• WIA-LD2ND: Wavelet-based Image Alignment for Self-supervised Low-Dose CT Denoising [74.14134385961775]
  We introduce a novel self-supervised CT image denoising method called WIA-LD2ND, only using NDCT data. WIA-LD2ND comprises two modules: Wavelet-based Image Alignment (WIA) and Frequency-Aware Multi-scale Loss (FAM)
  arXiv  Detail & Related papers  (2024-03-18T11:20:11Z)
• Rotational Augmented Noise2Inverse for Low-dose Computed Tomography Reconstruction [83.73429628413773]
  Supervised deep learning methods have shown the ability to remove noise in images but require accurate ground truth. We propose a novel self-supervised framework for LDCT, in which ground truth is not required for training the convolutional neural network (CNN) Numerical and experimental results show that the reconstruction accuracy of N2I with sparse views is degrading while the proposed rotational augmented Noise2Inverse (RAN2I) method keeps better image quality over a different range of sampling angles.
  arXiv  Detail & Related papers  (2023-12-19T22:40:51Z)
• LoMAE: Low-level Vision Masked Autoencoders for Low-dose CT Denoising [5.251624007533231]
  Masked autoencoders (MAE) have been recognized as an effective label-free self-pretraining method for transformers. We introduce an MAE-GradCAM method to shed light on the latent learning mechanisms of the MAE/LoMAE. Experiments show that the proposed LoMAE can enhance the transformer's denoising performance and greatly relieve the dependence on the ground truth clean data.
  arXiv  Detail & Related papers  (2023-10-19T01:34:30Z)
• On Sensitivity and Robustness of Normalization Schemes to Input Distribution Shifts in Automatic MR Image Diagnosis [58.634791552376235]
  Deep Learning (DL) models have achieved state-of-the-art performance in diagnosing multiple diseases using reconstructed images as input. DL models are sensitive to varying artifacts as it leads to changes in the input data distribution between the training and testing phases. We propose to use other normalization techniques, such as Group Normalization and Layer Normalization, to inject robustness into model performance against varying image artifacts.
  arXiv  Detail & Related papers  (2023-06-23T03:09:03Z)
• Treatment Learning Causal Transformer for Noisy Image Classification [62.639851972495094]
  In this work, we incorporate this binary information of "existence of noise" as treatment into image classification tasks to improve prediction accuracy. Motivated from causal variational inference, we propose a transformer-based architecture, that uses a latent generative model to estimate robust feature representations for noise image classification. We also create new noisy image datasets incorporating a wide range of noise factors for performance benchmarking.
  arXiv  Detail & Related papers  (2022-03-29T13:07:53Z)
• CTformer: Convolution-free Token2Token Dilated Vision Transformer for Low-dose CT Denoising [11.67382017798666]
  Low-dose computed tomography (LDCT) denoising is an important problem in CT research. vision transformers have shown superior feature representation ability over convolutional neural networks (CNNs) We propose a Convolution-free Token2Token Dilated Vision Transformer for low-dose CT denoising.
  arXiv  Detail & Related papers  (2022-02-28T02:58:16Z)
• Eformer: Edge Enhancement based Transformer for Medical Image Denoising [0.0]
  We present Eformer - Edge enhancement based transformer, a novel architecture that builds an encoder-decoder network. Non-overlapping window-based self-attention is used in the transformer block that reduces computational requirements.
  arXiv  Detail & Related papers  (2021-09-16T15:18:21Z)
• TED-net: Convolution-free T2T Vision Transformer-based Encoder-decoder Dilation network for Low-dose CT Denoising [5.2227817530931535]
  We propose a convolution-free vision transformer-based-decoder Dilation net-work (TED-net) to enrich the family of LDCT denoising algorithms. Our model is evaluated on the AAPM-Mayo clinic LDCT Grand Challenge dataset, and results show outperformance over the state-of-the-art denoising methods.
  arXiv  Detail & Related papers  (2021-06-08T19:26:55Z)
• Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations [68.8204255655161]
  Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods. We have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder. We show that by training the models on higher resolution images and by improving the quality of the reconstructions, we obtain results which are comparable with different baselines.
  arXiv  Detail & Related papers  (2020-07-24T14:02:09Z)

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.