This inventor holds 1 USPTO granted patent. Top assignee: Leland Stanford Junior University. Active years: 2024.
Company Filing History:
Years Active: 2024
Title: Innovations of Zhuoran Ma in Near-Infrared Fluorescence Imaging
Introduction
Zhuoran Ma is an accomplished inventor based in Stanford, California. He has made significant contributions to the field of imaging technology, particularly in enhancing near-infrared fluorescence imaging. His innovative approach utilizes deep learning techniques to improve the quality of fluorescence images, which has important implications for various scientific and medical applications.
Latest Patents
Zhuoran Ma holds a patent for a method titled "Deep learning for near-infrared fluorescence imaging enhancement." This patent describes a process that involves providing a near-infrared (NIR) fluorescence image produced by detecting light in the NIR-I or NIR-IIa windows emitted by fluorophores. The NIR fluorescence image is then inputted into a convolutional neural network, which produces a translated image as output. The convolutional neural network is trained using a set of NIR-I or NIR-IIa fluorescence images alongside a set of NIR-IIb fluorescence images. Notably, the convolutional neural network utilized in this method is a U-Net, which is known for its effectiveness in image processing tasks.
Career Highlights
Zhuoran Ma is affiliated with Leland Stanford Junior University, where he continues to advance his research and development in imaging technologies. His work has garnered attention for its innovative use of deep learning in enhancing imaging techniques, which can lead to improved diagnostic capabilities in medical fields.
Collaborations
Zhuoran Ma collaborates with esteemed colleagues, including Hongjie Dai, who is also recognized for his contributions to the field. Their joint efforts in research and development further enhance the impact of their work in imaging technology.
Conclusion
Zhuoran Ma's innovative contributions to near-infrared fluorescence imaging through deep learning techniques exemplify the potential of technology to transform scientific and medical imaging. His work not only advances the field but also opens new avenues for research and application.