OT-driven Multi-Domain Unsupervised Ultrasound Image Artifact Removal using a Single CNN

• URL: http://arxiv.org/abs/2007.05205v1
• Date: Fri, 10 Jul 2020 07:11:04 GMT
• Title: OT-driven Multi-Domain Unsupervised Ultrasound Image Artifact Removal using a Single CNN
• Authors: Jaeyoung Huh, Shujaat Khan, and Jong Chul Ye
• Abstract summary: Ultrasound imaging (US) often suffers from distinct image artifacts from various sources. We propose a novel, unsupervised, deep learning approach in which a single neural network can be used to deal with different types of US artifacts. Our algorithm is rigorously derived using an optimal transport (OT) theory for cascaded probability measures.
• Score: 36.574477281492
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ultrasound imaging (US) often suffers from distinct image artifacts from various sources. Classic approaches for solving these problems are usually model-based iterative approaches that have been developed specifically for each type of artifact, which are often computationally intensive. Recently, deep learning approaches have been proposed as computationally efficient and high performance alternatives. Unfortunately, in the current deep learning approaches, a dedicated neural network should be trained with matched training data for each specific artifact type. This poses a fundamental limitation in the practical use of deep learning for US, since large number of models should be stored to deal with various US image artifacts. Inspired by the recent success of multi-domain image transfer, here we propose a novel, unsupervised, deep learning approach in which a single neural network can be used to deal with different types of US artifacts simply by changing a mask vector that switches between different target domains. Our algorithm is rigorously derived using an optimal transport (OT) theory for cascaded probability measures. Experimental results using phantom and in vivo data demonstrate that the proposed method can generate high quality image by removing distinct artifacts, which are comparable to those obtained by separately trained multiple neural networks.

Related papers

• Unsupervised convolutional neural network fusion approach for change detection in remote sensing images [1.892026266421264]
  We introduce a completely unsupervised shallow convolutional neural network (USCNN) fusion approach for change detection. Our model has three features: the entire training process is conducted in an unsupervised manner, the network architecture is shallow, and the objective function is sparse. Experimental results on four real remote sensing datasets indicate the feasibility and effectiveness of the proposed approach.
  arXiv  Detail & Related papers  (2023-11-07T03:10:17Z)
• Multi-stage Deep Learning Artifact Reduction for Computed Tomography [0.0]
  We propose a multi-stage deep learning method for artifact removal, in which neural networks are applied to several domains. We show that the neural networks can be effectively trained in succession, resulting in easy-to-use and computationally efficient training.
  arXiv  Detail & Related papers  (2023-09-01T14:40:25Z)
• Improving Human-Object Interaction Detection via Virtual Image Learning [68.56682347374422]
  Human-Object Interaction (HOI) detection aims to understand the interactions between humans and objects. In this paper, we propose to alleviate the impact of such an unbalanced distribution via Virtual Image Leaning (VIL) A novel label-to-image approach, Multiple Steps Image Creation (MUSIC), is proposed to create a high-quality dataset that has a consistent distribution with real images.
  arXiv  Detail & Related papers  (2023-08-04T10:28:48Z)
• Domain Generalization for Mammographic Image Analysis with Contrastive Learning [62.25104935889111]
  The training of an efficacious deep learning model requires large data with diverse styles and qualities. A novel contrastive learning is developed to equip the deep learning models with better style generalization capability. The proposed method has been evaluated extensively and rigorously with mammograms from various vendor style domains and several public datasets.
  arXiv  Detail & Related papers  (2023-04-20T11:40:21Z)
• Unsupervised Domain Transfer with Conditional Invertible Neural Networks [83.90291882730925]
  We propose a domain transfer approach based on conditional invertible neural networks (cINNs) Our method inherently guarantees cycle consistency through its invertible architecture, and network training can efficiently be conducted with maximum likelihood. Our method enables the generation of realistic spectral data and outperforms the state of the art on two downstream classification tasks.
  arXiv  Detail & Related papers  (2023-03-17T18:00:27Z)
• Traditional Classification Neural Networks are Good Generators: They are Competitive with DDPMs and GANs [104.72108627191041]
  We show that conventional neural network classifiers can generate high-quality images comparable to state-of-the-art generative models. We propose a mask-based reconstruction module to make semantic gradients-aware to synthesize plausible images. We show that our method is also applicable to text-to-image generation by regarding image-text foundation models.
  arXiv  Detail & Related papers  (2022-11-27T11:25:35Z)
• SiPRNet: End-to-End Learning for Single-Shot Phase Retrieval [8.820823270160695]
  convolutional neural networks (CNN) have played important roles in various image reconstruction tasks. In this paper, we design a novel CNN structure, named SiPRNet, to recover a signal from a single Fourier intensity measurement. The proposed approach consistently outperforms other CNN-based and traditional optimization-based methods in single-shot maskless phase retrieval.
  arXiv  Detail & Related papers  (2022-05-23T16:24:52Z)
• Adaptive Convolutional Dictionary Network for CT Metal Artifact Reduction [62.691996239590125]
  We propose an adaptive convolutional dictionary network (ACDNet) for metal artifact reduction. Our ACDNet can automatically learn the prior for artifact-free CT images via training data and adaptively adjust the representation kernels for each input CT image. Our method inherits the clear interpretability of model-based methods and maintains the powerful representation ability of learning-based methods.
  arXiv  Detail & Related papers  (2022-05-16T06:49:36Z)
• Convolutional Neural Networks for Multispectral Image Cloud Masking [7.812073412066698]
  Convolutional neural networks (CNN) have proven to be state of the art methods for many image classification tasks. We study the use of different CNN architectures for cloud masking of Proba-V multispectral images.
  arXiv  Detail & Related papers  (2020-12-09T21:33:20Z)
• Exploring Intensity Invariance in Deep Neural Networks for Brain Image Registration [0.0]
  We investigate the effect of intensity distribution among input image pairs for deep learning-based image registration methods. Deep learning models trained with structure similarity-based loss seems to perform better for both datasets.
  arXiv  Detail & Related papers  (2020-09-21T17:49:03Z)

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.