Cedar Rapids, IA, United States of America

Warrent Claride


Average Co-Inventor Count = 4.0

ph-index = 1

Forward Citations = 5(Granted Patents)


Company Filing History:


Years Active: 2018

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1 patent (USPTO):Explore Patents

Title: Innovations by Warrent Claride in Retinal Image Analysis

Introduction

Warrent Claride is an innovative inventor based in Cedar Rapids, Iowa. He has made significant contributions to the field of retinal image analysis through his patented technology. His work focuses on enhancing the capabilities of neural networks to detect features in retinal images, which can have profound implications for medical diagnostics.

Latest Patents

Warrent Claride holds a patent for "Systems and methods for feature detection in retinal images." This patent outlines a method for training a neural network to effectively identify features within retinal images. The process involves creating and randomizing training and testing datasets, training multiple neural networks with varying architectures, and iteratively refining the model based on performance metrics. This innovative approach aims to minimize false positives and negatives, ultimately improving the accuracy of retinal image analysis.

Career Highlights

Warrent Claride is associated with Idx, LLC, where he applies his expertise in neural networks and image processing. His work at Idx, LLC has positioned him as a key player in the development of advanced imaging technologies. His dedication to innovation is evident in his approach to solving complex problems in the field of retinal imaging.

Collaborations

Warrent collaborates with talented individuals such as Meindert Niemeijer and Ryan Amelon. These collaborations enhance the research and development efforts at Idx, LLC, fostering an environment of creativity and innovation.

Conclusion

Warrent Claride's contributions to retinal image analysis through his patented technology exemplify the impact of innovative thinking in the medical field. His work not only advances the capabilities of neural networks but also holds the potential to improve diagnostic processes in healthcare.

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