This inventor holds 1 USPTO granted patent. Top assignee: The United States of America, as Represented by the Secretary, Department of Health and Human Services. Active years: 2021.
Company Filing History:
Years Active: 2021
Title: Holger Roth: Innovator in Prostate Cancer Detection
Introduction
Holger Roth is a distinguished inventor based in Bavaria, Germany. He has made significant contributions to the field of medical imaging, particularly in the detection of prostate cancer. His innovative work focuses on utilizing advanced machine learning techniques to enhance diagnostic accuracy.
Latest Patents
Holger Roth holds a patent titled "Detection of prostate cancer in multi-parametric MRI using random forest with instance weighting and MR prostate segmentation by deep learning with holistically-nested networks." This patent describes a computer-aided diagnosis (CAD) system that employs a Random Forest classifier to detect prostate cancer. The system classifies individual pixels within the prostate as potential cancer sites by utilizing a combination of spatial, intensity, and texture features extracted from three MRI sequences. The Random Forest training incorporates instance-level weighting to ensure equal treatment of both small and large cancerous lesions, as well as varying prostate backgrounds. Additionally, the patent outlines methods for accurate automatic segmentation of the prostate in MRI, employing both patch-based and holistic deep learning techniques.
Career Highlights
Holger Roth is currently associated with the United States of America, as represented by the Secretary, Department of Health and Human Services. His work has been pivotal in advancing the capabilities of prostate cancer detection through innovative imaging techniques.
Collaborations
Holger has collaborated with notable professionals in his field, including Sonia Gaur and Matthew J McAuliffe. Their combined expertise has contributed to the development of cutting-edge diagnostic tools.
Conclusion
Holger Roth's contributions to prostate cancer detection through innovative machine learning techniques exemplify the potential of technology in improving healthcare outcomes. His work continues to pave the way for advancements in medical imaging and diagnosis.