Toronto, Canada

Jimmy Ba



Average Co-Inventor Count = 3.0

ph-index = 1

Forward Citations = 9(Granted Patents)


Company Filing History:


Years Active: 2019

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1 patent (USPTO):Explore Patents

Title: The Innovative Contributions of Jimmy Ba

Introduction

Jimmy Ba is a prominent inventor based in Toronto, Canada. He has made significant contributions to the field of microscopy through his innovative patent. His work focuses on the application of deep learning techniques to enhance the analysis of microscopy images.

Latest Patents

Jimmy Ba holds a patent titled "System and method for classifying and segmenting microscopy images with deep multiple instance learning." This system receives microscopy images as input, extracts features, and applies layers of processing units to compute a set of cellular phenotype features. These features correspond to cellular densities and fluorescence measured under various conditions. The architecture of the system includes a convolutional neural network followed by a multiple instance learning pooling layer. Notably, the system does not require segmentation steps or per cell labels, as it can be trained and tested directly on raw microscopy images in real-time. It computes class-specific feature maps for every phenotype variable using a fully convolutional neural network and aggregates across these feature maps using multiple instance learning. The system ultimately produces predictions for one or more reference cellular phenotype variables based on populations of cells captured in microscopy images.

Career Highlights

Jimmy Ba is associated with Phenomic AI Inc., where he applies his expertise in deep learning and microscopy. His innovative approach has positioned him as a key figure in the intersection of artificial intelligence and biological imaging.

Collaborations

He collaborates with notable colleagues, including Oren Kraus and Brendan John Frey, who contribute to the advancement of research in this field.

Conclusion

Jimmy Ba's contributions to the field of microscopy through his innovative patent demonstrate the potential of deep learning in biological research. His work continues to influence advancements in the analysis of cellular images, paving the way for future innovations.

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