Aubrey Chow

Title: Innovations in Medical Imaging by Aubrey Chow

Introduction

Aubrey Chow is an accomplished inventor based in Waterford, NY (US). She has made significant contributions to the field of medical imaging through her innovative work in deep reinforcement learning. Her research focuses on enhancing anatomical landmark detection, which is crucial for accurate medical diagnoses and treatments.

Latest Patents

Aubrey Chow holds a patent titled "Uncertainty-aware deep reinforcement learning for anatomical landmark detection in medical images." This patent describes techniques for uncertainty-aware anatomical landmark detection, utilizing a deep reinforcement learning (DRL) anatomical landmark detection agent. The process involves generating image features for an input medical image using a sub-network of the detection agent. A softmax layer of another sub-network generates discrete Q-value distributions for allowable actions associated with the agent's movement within the medical image. The predicted anatomical landmark location is determined using these Q-value distributions, with uncertainty assessed based on the average full width half maximum (FWHM) calculated for the distributions. This innovative approach enhances the reliability of landmark detection in medical imaging.

Career Highlights

Aubrey Chow is currently employed at Covera Health, Inc., where she continues to develop cutting-edge technologies in medical imaging. Her work is instrumental in improving the accuracy and efficiency of medical diagnoses through advanced computational techniques.

Collaborations

Aubrey collaborates with talented professionals in her field, including James Robert Browning and Li Zhang. These collaborations foster a dynamic environment for innovation and research.

Conclusion

Aubrey Chow's contributions to medical imaging through her patent and work at Covera Health, Inc. highlight her role as a leading inventor in the field. Her innovative techniques in deep reinforcement learning are paving the way for advancements in anatomical landmark detection, ultimately benefiting the medical community.