Fei Gao

Title: Fei Gao: Innovator in Deep Learning and Imaging Technologies

Introduction

Fei Gao is a prominent inventor based in Sunnyvale, California, known for his contributions to deep learning and imaging technologies. With a total of three patents to his name, he has made significant strides in the field of blob detection and imaging harmonization.

Latest Patents

One of his latest patents is titled "Deep learning based blob detection systems and methods." This innovative system utilizes deep learning to enhance blob detection capabilities. It includes a non-transitory computer-readable storage medium that stores instructions for training a U-Net and generating a probability map with centroids of corresponding blobs. The system also derives distance maps with bounded probabilities and applies Difference of Gaussian (DoG) with an adaptive scale, followed by Hessian analysis for blob segmentation.

Another notable patent is "Deep residual inception encoder-decoder network for amyloid PET harmonization." This invention employs deep learning to achieve imaging harmonization among images produced with different PET tracers. The method involves converting an original PET image of the brain into a target PET image that simulates what would be obtained using a different PET tracer.

Career Highlights

Fei Gao has worked with reputable organizations, including Arizona State University and Entit Software LLC. His experience in these institutions has allowed him to develop and refine his innovative ideas in the realm of deep learning and imaging technologies.

Collaborations

Fei has collaborated with notable colleagues such as Teresa Wu and Anurag Singla, contributing to advancements in their respective fields.

Conclusion

Fei Gao's work in deep learning and imaging technologies showcases his innovative spirit and dedication to advancing scientific knowledge. His patents reflect a commitment to improving blob detection and imaging harmonization, making significant contributions to the field.