Nathaniel Mason Braman

Title: Nathaniel Mason Braman: Innovator in Disease Detection Technology

Introduction

Nathaniel Mason Braman, an accomplished inventor based in Cleveland Heights, OH, has made significant contributions to the field of medical imaging and disease detection. With two patents to his name, he has played an instrumental role in advancing technologies that improve diagnostic capabilities in healthcare.

Latest Patents

Braman's latest innovations focus on developing disease detection models utilizing convolutional neural networks and long short-term memory architectures. One significant patent describes a method for detecting diseases from weakly annotated volumetric medical images. This innovative approach involves training a model using image studies and associated disease labels mined from radiology reports. The model features a convolutional neural network layer alongside a convolutional long short-term memory layer, allowing for sophisticated analysis of two-dimensional slices from three-dimensional image volumes. By extracting features from these slices and processing them, the model generates probabilities for disease presence, meticulously updating itself based on comparisons with the given labels.

Career Highlights

Nathaniel Mason Braman is affiliated with International Business Machines Corporation (IBM), a leading global technology company. His work at IBM highlights his deep involvement in the intersection of artificial intelligence and healthcare, pushing the boundaries of current technologies to enable better disease detection methods.

Collaborations

Throughout his career, Braman has collaborated with talented professionals, including Ehsan Dehghan Marvast and David James Beymer. These partnerships illustrate the collaborative spirit within the technology community, particularly in the pursuit of innovative healthcare solutions.

Conclusion

As an inventor, Nathaniel Mason Braman embodies the spirit of innovation in medical technology. His work continues to contribute towards more efficient and accurate disease detection models, potentially transforming how medical professionals interpret imaging studies. With his ongoing efforts and collaborations, he is poised to influence the future of healthcare technology significantly.