Muhammad Muzammal Naseer

Title: Innovations by Muhammad Muzammal Naseer

Introduction

Muhammad Muzammal Naseer is an accomplished inventor based in Abu Dhabi, AE. He has made significant contributions to the field of deep learning and medical imaging, holding 3 patents that showcase his innovative approach to technology.

Latest Patents

His latest patents include a "System and method of training vision transformer on small-scale datasets." This invention features a deep learning training system that incorporates an imaging system for capturing medical images, a machine learning engine, and a display. The machine learning engine selects a small-scale of images from a training dataset, generates global views by randomly selecting regions in one image, and generates local views by randomly selecting regions covering less than a majority of the image. The system trains parameters in a student-teacher network to predict a class of objects through self-supervised view prediction.

Another notable patent is the "System and method for self-distilled vision transformer for domain generalization." This apparatus trains a vision transformer neural network using a training dataset that includes at least two domains for diagnosing medical conditions. The vision transformer neural network is optimized based on the difference between the predicted class token and sub-model predictions, allowing for effective inferencing in target domains.

Career Highlights

Muhammad has worked at prestigious institutions, including Mohamed Bin Zayed University of Artificial Intelligence. His work focuses on advancing machine learning techniques and their applications in medical diagnostics.

Collaborations

He has collaborated with notable colleagues such as Maryam Sultana and Muhammad Haris Khan, contributing to the development of innovative solutions in his field.

Conclusion

Muhammad Muzammal Naseer is a pioneering inventor whose work in deep learning and medical imaging continues to push the boundaries of technology. His patents reflect a commitment to improving diagnostic methods through innovative machine learning techniques.