Shuchen Zhang

Title: Shuchen Zhang: Innovator in Medical Imaging Technology

Introduction

Shuchen Zhang is a prominent inventor based in Champaign, IL, specializing in advancements in medical imaging technology. He has made significant contributions to the field, particularly in the development of innovative methods for image classification and analysis.

Latest Patents

Shuchen Zhang holds 2 patents that showcase his expertise. His latest patents include a method titled "Binomial subsample data augmented CNN for image classification." This method involves automatically classifying emission tomographic images by utilizing a data generator to create generated images based on original images. The process includes shuffling the original images and applying geometric transformations, ultimately training a multi-layer convolutional neural network (CNN) to classify input images.

Another notable patent is "CNN-based abnormality score map for SPECT gamma camera flood analysis." This method focuses on determining an abnormality score map for Single-photon Emission Computed Tomography (SPECT) gamma camera flood analysis. It involves extracting image patches from an input flood image and generating feature vectors to create a per-patch abnormality score, which is then used to depict the overall abnormality scores for the input flood image.

Career Highlights

Shuchen Zhang is currently employed at Siemens Medical Solutions USA, Inc., where he continues to push the boundaries of medical imaging technology. His work has been instrumental in enhancing the accuracy and efficiency of image classification and analysis in the medical field.

Collaborations

Some of his notable coworkers include Xinhong Ding and Leonard Anzelde, who contribute to the collaborative efforts in advancing medical imaging solutions.

Conclusion

Shuchen Zhang's innovative work in medical imaging technology, particularly through his patents, highlights his significant impact on the field. His contributions continue to pave the way for advancements in image classification and analysis, ultimately benefiting medical professionals and patients alike.