A Regularized Conditional GAN for Posterior Sampling in Inverse Problems
TS-063238 — A novel regularization technique applicable for medical imaging applications that leverages conditional generative adversarial networks (cGANs) to generate reconstructed images in significantly shorter timeframes.
The Need
Several techniques are used for image reconstruction in the medical aren…
- College: College of Engineering (COE)
- Inventors: Bendel, Matthew; Ahmad, Rizwan; Schniter, Philip
- Licensing Officer: Hampton, Andrew
Convolutional Neural Network to Assess Phayngeal and Laryngeal Pathology and Function on Nasopharyngolaryngoscopy
TS-063154 — Worldwide, 686,000 new head and neck (H&N) cancers are diagnosed yearly, and 375,000 people will die annually. Human papillomavirus (HPV) is responsible for an increasing subset of H&N malignancies called oropharyngeal squamous cell carcinomas (OPSCC). Although it has a better prognosis than…
- College: College of Engineering (COE)
- Inventors: Krening, Samantha; Gifford, Ryan; Jhawar, Sachin; VanKoevering, Kyle
- Licensing Officer: Hampton, Andrew
Fresh Caching of Dynamic Content: Algorithm and Implementation
TS-063024 — The Need
In today's data-driven world, efficient caching of dynamic content is crucial for delivering timely and responsive services. Conventional caching methods struggle to adapt to frequent updates in the back-end database, leading to suboptimal system costs and decreased user satisfaction. …
- College: College of Engineering (COE)
- Inventors: Abolhassani, Bahman; Eryilmaz, Atilla
- Licensing Officer: Hampton, Andrew
Pheno - A modern, multi-faceted, data-driven technology platform for addressing low back and neck pain
TS-052847 —
Back and neck pain is one of the world’s biggest health problems, and one which most people will experience during some stage in their lives. An estimated 80% of people will face this issue, although the severity of the pain may differ. Approximately 50 billion USD are spent by Americans eac…
- College: College of Engineering (COE)
- Inventors: Marras, William; Aurand, Alexander "Alex"; Dufour, Jonathan; Knapik, Gregory; Prasath, Prasath Mageswaran
- Licensing Officer: Hampton, Andrew
Finding rare events by knowing where NOT to look
TS-051417 —
Traditional regression analysis has been a staple in predicting cause-effect relationships. Counltess industries use these methods to predict rare events, from economic issues to cancer research, causality is often a desired result. Given the vast amount of potential variables it sometimes becomes…
- College: College of Arts & Sciences
- Inventors: Melamed, David; Schoon, Eric
- Licensing Officer: Hampton, Andrew
Virtual image generation of immunohistochemical stained tissue sections using an artificial neural network
TS-039781 — Realistic immunohistochemical stained tissue section databases for training, certification, and machine standards.
Immunohistochemistry (IHC) is used to determine antigen distribution in a tissue and is widely used for diagnosis of cancers and other diseases. Diagnostic and research laboratories in the United States use locally devised IHC tissue slide preparation and scanning protocols to check scanner perfor…
- College: College of Medicine (COM)
- Inventors: Gurcan, Metin; Lozanski, Gerard; Senaras, Caglar
- Licensing Officer: Hampton, Andrew
At-Home Digital Platform for Scoring Skin Disease
TS-038429 —
An automated image analysis system for the recognition and quantification of skin disease
Acne and rosacea are skin diseases that affect around 85% of individuals, the former being the most common skin condition afflicting up to 50 million people. There is no gold standard for evaluation of these skin diseases, and their treatment efficacy is generally determined according to a poorly …
- College: College of Medicine (COM)
- Inventors: Kaffenberger, Benjamin; Gurcan, Metin
- Licensing Officer: Hampton, Andrew