Image Processing

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  Image reconstruction by domain-transform manifold learning

  • Published on: March, 2017
  • Bruce R. Rosen, Jeremiah Z. Liu, Matthew S. Rosen, Bo Zhu
  • Code Available

  Magnetic resonance imaging (MRI), X-ray computed tomography (CT), positron emission tomography (PET), ultrasound, and radio astronomy all rely on imag ...

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  Deep CT to MR Synthesis Using Paired and Unpaired Data

  • Published on: May, 2019
  • Wonmo Jung, In Ho Han, Hakil Kim, Xuenan Cui, Cheng-Bin Jin, Jae Il Lee, Seongsu Joo, Mingjie Liu, Eunsik Park...
  • Code Available

  Magnetic resonance (MR) imaging plays a highly important role in radiotherapy treatment planning for the segmentation of tumor volumes and organs. How ...

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  Unsupervised Domain Adaptation from Axial to Short-Axis Multi-Slice Cardiac MR Images by Incorporating Pretrained Task Networks

  • Published on: January, 2021
  • Heiner Latus, Ivo Wolf, Tarique Hussain, Samir Sarikouch, Gerald Greil, Florian Ritzmann, Tyler Huffaker, Zach...
  • Code Available

  The model uses Unsupervised Domain Adaptation (UDA) of 3D cardiac magnetic resonance (CMR) images to transform from axial to short-axis orientation an ...

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  Low Dose CT Image Denoising using A Generative Adversarial Network with Wasserstein Distance and Perceptual Loss

  • Published on: April, 2018
  • Mannudeep K. Kalra, Hengyong Yu, Member, Xuanqin Mou, Pingkun Yan, Qingsong Yang, Senior Member, Yi Zhang Memb...
  • Code Available

  The continuous development and extensive use of CT in medical practice has raised a public concern over the associated radiation dose to the patient. ...

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  MRI Brain Tumor Segmentation and Uncertainty Estimation using 3D-UNet Architectures

  • Published on: September, 2020
  • Veronica Vilaplana, Laura Mora Ballestar
  • Code Available

  Convolutional neural networks have shown significant results in brain tumor segmentation. Automation of brain tumor segmentation can be very useful fo ...

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  Hierarchical 3D fully convolutional networks for multi-organ segmentation

  • Published on: April, 2017
  • Michitaka Fujiwara, Holger R. Roth, Hirohisa Oda1, and Kensaku Mori, Masahiro Oda, Natsuki Shimizu, Kazunari M...
  • Code Available

  The model demonstrates how hierarchical deployment of 3D CNN based on a fully convolutional architecture (3D U-Net) can produce competitive results fo ...

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  XNet A convolutional neural network CNN implementation for medical XRay image segmentation suitable for small datasets

  • Published on: December, 2018
  • Joseph Aylett-Bullock, Carolina Cuesta, Arnau Quera-Bofarull
  • Code Available

  XNet is a Convolutional Neural Network designed for the segmentation of X-Ray images into bone, soft tissue and open beam regions. Specifically, it pe ...

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  Probabilistic Noise2Void: Unsupervised Content-Aware Denoising

  • Published on: February, 2020
  •  Tomas Vicar,  Florian Jug, Alexander Krull
  • Code Available

  Convolutional Neural Network algorithm is leading technic for image denoising. Usually CNNs are used on pair of images, but it is impractical and diff ...

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  Automated radiology report generation using conditioned transformers

  • Published on: March, 2021
  • Omar Alfarghaly and Rana Khaled and Abeer Elkorany and Maha Helal and Aly Fahmy
  • Code Available

  Automatically generate full radiology reports given chest X-ray images from the IU-X-Ray dataset by conditioning a recurrent neural net on the visual ...

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  TransBTS: Multimodal Brain Tumor Segmentation Using Transformer

  • Published on: March, 2021
  • Wenxuan Wang, Hong Yu, Jiangyun Li, Chen Chen, Meng Ding, Sen Zha
  • Code Available

  TransBTS: Multimodal Brain Tumor Segmentation Using Transformer presents a novel segmentation framework that effectively incorporates Transformer in 3 ...