Company Filing History:
Years Active: 2024-2025
Title: Innovations of Jiaxuan Pang in Medical Imaging
Introduction
Jiaxuan Pang is an accomplished inventor based in Tempe, Arizona, known for his significant contributions to the field of medical imaging. With a total of two patents, he has developed innovative systems and methods that enhance the capabilities of artificial intelligence in medical diagnostics.
Latest Patents
Jiaxuan Pang's latest patents include groundbreaking technologies aimed at improving medical image classification. The first patent focuses on systems, methods, and apparatuses for implementing self-supervised domain-adaptive pre-training via a transformer. This invention allows for the pre-training of an AI model using non-medical photographic images, followed by domain-adaptive pre-training with medical images. The trained model can then generate predicted medical diagnoses from input images not present in the training data.
The second patent involves the generation of self-taught models, named Models Genesis, which do not require manual labeling for processing medical imaging. This system is designed to learn general-purpose image representations by recovering original sub-volumes of 3D input images from transformed images. The pre-trained 3D generic model significantly improves performance across various target tasks, diseases, organs, datasets, and modalities.
Career Highlights
Jiaxuan Pang is affiliated with Arizona State University, where he continues to push the boundaries of research in medical imaging and artificial intelligence. His work has garnered attention for its innovative approach to solving complex problems in the medical field.
Collaborations
Jiaxuan has collaborated with notable colleagues, including Jianming Liang and Zongwei Zhou, who contribute to the advancement of his research initiatives.
Conclusion
Jiaxuan Pang's innovative work in medical imaging exemplifies the potential of artificial intelligence to transform healthcare diagnostics. His patents reflect a commitment to enhancing the accuracy and efficiency of medical image analysis, paving the way for future advancements in the field.