Image Processing

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  Automatic Body Part Identification in Whole-Body MRI

  • Published on: October, 2020
  • a-doering
  • Code Available

  The study of medical imaging data from large cohort studies, such as Magnetic Resonance Imaging (MRI), takes a long time. As a result, model for autom ...

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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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  PIANO - A Parametric Hand Bone Model from Magnetic Resonance Imaging

  • Published on: June, 2021
  • Yuwei Li, Minye Wu, Yuyao Zhang, Lan Xu and Jingyi Yu
  • Code Available

  Hand modeling is critical for immersive VR/AR, action understanding, or human healthcare. Existing parametric models account only for hand shape, pose ...

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  FracNet Rib fracture detection system based on deep learning

  • Published on: December, 2020
  • Jiancheng Yang, Yingli Sun, Kaiming Kuang, Hui Kang, Yiyi Gao, Liang Jin, Pan Gao, Bingbing Ni, Mingyu Tan, We...
  • Code Available

  Diagnosis of rib fractures plays an important role in identifying trauma severity. However, quickly and precisely identifying the rib fractures in a l ...

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  GENERATIVE ADVERSARIAL NETWORKS For IMAGE-TO-IMAGE Translation on MULTI-CONTRAST MR IMAGES - A Comparison OF CycleGAN and UNIT

  • Published on: June, 2018
  • Anders Eklund, Per Welander, Simon Karlsson
  • Code Available

  Model uses application of GANs, image-to-image translations, which can be used to obtain multimodal datasets from a single modality. Two unsupervised ...

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  Deep Learning Based Sparse Whole Slide Image Analysis for the Diagnosis of Gastric Intestinal Metaplasia

  • Published on: May, 2022
  • Stephanie Zhang, Jon Braatz, Jeanne Shen, Pranav Rajpurkar, Andrew Y. Ng
  • Code Available

  In recent years, deep learning has successfully been applied to automate a wide variety of tasks in diagnostic histopathology. However, fast and relia ...