no code implementations • 17 Mar 2022 • Erim Yanik, Uwe Kruger, Xavier Intes, Rahul Rahul, Suvranu De
To ensure satisfactory clinical outcomes, surgical skill assessment must be objective, time-efficient, and preferentially automated - none of which is currently achievable.
no code implementations • 3 Mar 2021 • Erim Yanik, Xavier Intes, Uwe Kruger, Pingkun Yan, David Miller, Brian Van Voorst, Basiel Makled, Jack Norfleet, Suvranu De
Here, we use the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to systematically survey the literature on the use of Deep Neural Networks for automated and objective surgical skill assessment, with a focus on kinematic data as putative markers of surgical competency.
no code implementations • 5 Mar 2019 • Grant Haskins, Uwe Kruger, Pingkun Yan
This survey, therefore, outlines the evolution of deep learning based medical image registration in the context of both research challenges and relevant innovations in the past few years.
1 code implementation • 20 Feb 2019 • Hengtao Guo, Uwe Kruger, Ge Wang, Mannudeep K. Kalra, Pingkun Yan
Recent studies have highlighted the high correlation between cardiovascular diseases (CVD) and lung cancer, and both are associated with significant morbidity and mortality.
1 code implementation • 8 Nov 2018 • Hongming Shan, Atul Padole, Fatemeh Homayounieh, Uwe Kruger, Ruhani Doda Khera, Chayanin Nitiwarangkul, Mannudeep K. Kalra, Ge Wang
Here we design a novel neural network architecture for low-dose CT (LDCT) and compare it with commercial iterative reconstruction methods used for standard of care CT.
no code implementations • 12 Jun 2018 • Grant Haskins, Jochen Kruecker, Uwe Kruger, Sheng Xu, Peter A. Pinto, Brad J. Wood, Pingkun Yan
Conclusion: A similarity metric that is learned using a deep neural network can be used to assess the quality of any given image registration and can be used in conjunction with the aforementioned optimization framework to perform automatic registration that is robust to poor initialization.
no code implementations • 15 Feb 2018 • Hongming Shan, Yi Zhang, Qingsong Yang, Uwe Kruger, Mannudeep K. Kalra, Ling Sun, Wenxiang Cong, Ge Wang
Based on the transfer learning from 2D to 3D, the 3D network converges faster and achieves a better denoising performance than that trained from scratch.