Phoenix, AZ, United States of America

Zuwei Guo

This inventor holds 1 USPTO granted patent and 2 published patent applications. Top assignee: Arizona State University. Active years: 2026.

USPTO Granted Patents = 1 

Average Co-Inventor Count = 1.0

ph-index = 1


Company Filing History:


Years Active: 2026

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1 patent (USPTO):Explore Patents

Title: Zuwei Guo: Innovator in Medical Image Analysis

Introduction

Zuwei Guo is a prominent inventor based in Phoenix, AZ (US). He has made significant contributions to the field of medical image analysis through his innovative patent. His work focuses on enhancing the capabilities of machine learning in processing medical images.

Latest Patents

Zuwei Guo holds a patent titled "Systems, methods, and apparatuses for implementing discriminative, restorative, and adversarial (DiRA) learning using stepwise incremental pre-training for medical image analysis." This patent describes a system that receives a plurality of medical images and integrates Self-Supervised Machine Learning (SSL) instructions. The model processes these images through a combination of discriminative, restorative, and adversarial learning operations. The architecture includes a discriminative encoder, a restorative decoder, and an adversarial encoder, all designed to work together in a stepwise incremental training process.

Career Highlights

Zuwei Guo is affiliated with Arizona State University, where he continues to advance research in medical image analysis. His innovative approach to machine learning has positioned him as a key figure in this domain. He has successfully developed methods that improve the accuracy and efficiency of medical image processing.

Collaborations

Zuwei Guo collaborates with notable colleagues, including Nahid Ul Islam and Jianming Liang. Their combined expertise contributes to the advancement of research and innovation in medical imaging technologies.

Conclusion

Zuwei Guo's contributions to medical image analysis through his patent demonstrate his commitment to innovation in the field. His work not only enhances the capabilities of machine learning but also has the potential to significantly impact medical diagnostics.

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