Search Results for author: Uwe Kruger

Found 7 papers, 2 papers with code

Video-based Formative and Summative Assessment of Surgical Tasks using Deep Learning

no code implementations17 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.

Deep Neural Networks for the Assessment of Surgical Skills: A Systematic Review

no code implementations3 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.

Deep Learning in Medical Image Registration: A Survey

no code implementations5 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.

Image Registration Medical Image Registration

Knowledge-based Analysis for Mortality Prediction from CT Images

1 code implementation20 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.

Clinical Knowledge Lung Cancer Diagnosis +1

Can Deep Learning Outperform Modern Commercial CT Image Reconstruction Methods?

1 code implementation8 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.

Denoising Image Reconstruction

Learning Deep Similarity Metric for 3D MR-TRUS Registration

no code implementations12 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.

Image Registration

3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Network

no code implementations15 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.

Computed Tomography (CT) Decoder +3

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