Phuc Nguyen

Phuc Nguyen

Undergraduate student researcher at the University of Cincinnati

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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

Future Blog Post

less than 1 minute read

Published:

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Blog Post number 4

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 2

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

gsoc

Final report

This is the end of my GSoC journey (August 26, 2023).

portfolio

projects

Quality control of brain tumor segmentations

Deep learning models have been extensively developed for brain tumor segmentations from MRI images, mostly in a supervised way where ground truth labels are available. However, this condition is not warranted in datasets that do not have ground truth annotations. In this work, we developed a novel method that predicted the Dice score of the tumor segmentations without the ground truth, while also generating the error map that highlighted the areas where the predicted segmentation and the ground truth disagreed.

Realistic generation of synthetic pediatric liver ultrasound images for liver disease classification

Large-scale labeled biomedical datasets are difficult to access, and these datasets remain small in the medical imaging domain compared to other common computer visiom datasets. Therefore, we presented a method of using different models of generative adversarial networks to generate synthetic liver ultrasound images. Our results showed that the synthetic images had good quality compared with the real images, and this synthetic dataset would be promising as a data augmentation method for liver disease classification task.

publications

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.