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Atlanta, GA | Posted: July 28, 2017
Jerry Grillo
Communications Officer II
Parker H. Petit Institute for
Bioengineering and Bioscience
Summary Sentence:
Ph.D. candidate from Jaydev Desai’s robotics lab earns top honor for best student paper
Full Summary:
Ph.D. candidate from Jaydev Desai’s robotics lab earns top honor for best student paper
Kihan ParkKihan Park, a graduate student in the RoboMed Lab directed by Jaydev Desai, won the Best Student Paper award at the International Conference of Manipulation, Automation, and Robotics at Small Scales (MARSS 2017), July 17-21, in Montreal, Canada.
The research, entitled “Machine Learning Approach to Breast Cancer Localization,” was authored by Park, in the third year of his Ph.D. studies, and Desai, professor in the Wallace H. Coulter Department of Biomedical Engineering.
“In this paper, a feasible way of breast cancer localization at the micro-scale using tissue indentation and machine learning is presented,” explains Park, whose hometown is Daejeon, South Korea, where he completed his undergraduate and master’s degrees in mechanical engineering at the Korea Advanced Institute of Science and Technology. “This approach consists of two main parts, namely, obtaining mechanical signatures of breast tissue through micro-indentation and applying machine learning algorithms to the experimental data for cancer diagnosis.”
The worldwide prevalence of breast cancer was the main inspiration behind Park’s research.
“Breast cancer is the most common type of cancer among women and early diagnosis is a key factor for increasing survival rates and improving patients’ quality of life,” he says.
In Desai’s lab, researchers have found several biomarkers of breast cancer, “such as mechanical, electrical, and thermal properties of breast tissue,” says Park.
“We have been inspired machine learning, which is a powerful tool for data analysis and classification to utilize those biomarkers more effectively in breast cancer diagnosis,” he adds. “We are now moving forward to automate the breast cancer diagnostic process by combining micro-scale characterization with machine learning.”
