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Skip to Search Results- 15Boulanger, Pierre (Computing Science)
- 1Bischof, Walter F. (Computing Science)
- 1Menon, Geetha (Oncology)
- 1Punithakumar, Kumar (Radiology, Computing Science)
- 1Punithakumar, Kumaradevan (Radiology & Diagnostic Imaging)
- 1Punithakumar, Kumaradevan (Radiology and Diagnostic Imaging)
- 2Abdi Oskouie, Mina
- 1Anderson, Fraser
- 1Aziz, Muhammad Usman
- 1Charmchi, Sadegh
- 1Diaz, Idanis Beatriz
- 1Fatemi Pour, Farnoosh
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Fall 2018
The left ventricle segmentation is an important medical imaging task necessary to measure a patient's heart pumping efficiency. Recently, convolutional neural networks (CNN) have shown great potential in achieving state-of-the-art segmentation for such applications. However, most of the research...
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Predicting Uterine Deformation Due to Applicator Insertion in Pre-Brachytherapy MRI Using Deep Learning
DownloadSpring 2023
In locally advanced cervical cancer (LACC), brachytherapy (BT) remains the gold standard for boosting to curative doses in radiotherapy. Progress towards balancing target and routine tissue dosimetry for better clinical outcomes has been made possible by magnetic resonance imaging (MRI)-guided...
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Fall 2013
This thesis considers the problem of visualizing simulations of phenomenon which span large ranges of spatial scales. These datasets tend to be extremely large presenting challenges both to human comprehension and high-performance computing. The main problems considered are how to effectively...
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Fall 2015
Free Viewpoint Video and TV is regarded as the future of digital entertainment allowing users to navigate through multiple video streams of an event to select novel viewpoints. This new capability will be able to give to the users the illusion that they are present at the event. In this thesis,...
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Spring 2021
This thesis evaluated Convoultional LSTM (ConvLSTM) for frame prediction to help better understand motion in neural networks. Three different neural networks were implemented and trained. The three networks included, the original ConvLSTM paper by Shi et al. [35], the Spatio-Temporal network by...