This inventor holds 2 USPTO granted patents. Top assignees: Dotter Co., Ltd., Dotter Inc.. Active years: 2024-2026.
Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile
Company Filing History:
Years Active: 2024-2026
Title: Hyung-Il Kim: Innovator in Image Segmentation Technology
Introduction
Hyung-Il Kim is a prominent inventor based in Incheon, South Korea. He has made significant contributions to the field of image processing, particularly through his innovative work in deep learning technologies. His expertise has led to the development of advanced methods that enhance the capabilities of medical imaging.
Latest Patents
Hyung-Il Kim holds a patent for a "Deep learning based image segmentation method including biodegradable stent in intravascular optical tomography image." This patent discloses an image segmentation device that includes a storage unit for a segmentation model, which is designed to segment predetermined objects. The device features a processor that inputs data into the segmentation model, allowing for effective segmentation of objects within the input data. The segmentation model comprises an encoder with dimension reduction blocks and a decoder with dimension increase blocks, along with an auxiliary classification model for object recognition.
Career Highlights
Hyung-Il Kim is associated with Dotter Co., Ltd., where he applies his expertise in image segmentation and deep learning. His work has been instrumental in advancing the technology used in medical imaging, particularly in the context of intravascular optical tomography.
Collaborations
Hyung-Il Kim collaborates with notable colleagues, including Hyeong-Soo Nam and Aleksandr Dolgov. Their combined efforts contribute to the innovative projects at Dotter Co., Ltd., enhancing the company's research and development capabilities.
Conclusion
Hyung-Il Kim's contributions to image segmentation technology exemplify the impact of innovation in medical imaging. His work continues to pave the way for advancements in the field, showcasing the importance of deep learning in enhancing diagnostic capabilities.
Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile