Company Filing History:
Years Active: 2024
Title: Innovations by Pinal Bavishi in Fundus Image Processing
Introduction
Pinal Bavishi is an accomplished inventor based in Mountain View, CA. He has made significant contributions to the field of medical imaging, particularly in the processing of fundus camera images using advanced machine learning techniques. His innovative work aims to enhance the accuracy and efficiency of diagnosing eye conditions.
Latest Patents
Pinal Bavishi holds a patent for a groundbreaking method titled "Processing fundus camera images using machine learning models trained using other modalities." This patent encompasses methods, systems, and apparatus, including computer programs encoded on computer storage media. The invention focuses on training machine learning models to process fundus images captured by a fundus camera, ultimately generating predicted labels for these images. The process involves generating training data by receiving sets of training fundus images and corresponding ground truth labels assigned to different images of the eye captured using various imaging modalities. This innovative approach aims to improve the training of machine learning models for better image processing outcomes.
Career Highlights
Pinal Bavishi is currently employed at Google Inc., where he continues to push the boundaries of technology in the healthcare sector. His work at Google has allowed him to collaborate with some of the brightest minds in the industry, contributing to advancements in machine learning and medical imaging.
Collaborations
Pinal has worked alongside talented colleagues, including Lily Hao Yi Peng and Dale R Webster. Their collaborative efforts have fostered an environment of innovation and creativity, leading to significant advancements in their respective fields.
Conclusion
Pinal Bavishi's contributions to the field of fundus image processing exemplify the intersection of technology and healthcare. His innovative patent and work at Google Inc. highlight the potential of machine learning in improving medical diagnostics.