Vacancy (Research Scholarship Scheme)

Project Title: Innovate Histopathological Glioblastoma Diagnosis using Deep Machine Learning Image Analysis
Location: Sungai Long campus
Appointment Duration: 1 year
No. of Vacancy: 1 PhD candidate
Stipend: RM 3,000 per month (Full Tuition Fee Scholarship for candidate with CGPA: 3.5000 and above T&C applies;)
Education Level: Minimum Bachelor degree with CGPA of 3.67 and above or the first class equivalent, or Master degree
Specific Skills / Knowledge Required: Machine learning, Python/Matlab, familiar with Linux Operating System
Job Description:
  1. Pathology diagnosis has been performed by a human pathologist by observing the stained specimen on the slide glass using a microscope for decades. In recent years, many efforts and technologies have been developed to digitalize the entire pathological slides so that a computer can be used to facilitate this labor intensive manual labeling and classification of pathological slides. Many attempts have been made to analyze these digital slices using digital image analysis based on machine learning algorithms to assist tasks including diagnosis. However, most of the digital pathological image analysis often uses general image recognition technology, for example facial recognition as a basis. Since digital pathological images and tasks have some unique characteristics and are vastly different from the facial features, special processing techniques are often required, leading to the study of this work to design a new machine learning technique specifically for histopathological images to differentiate glioblastoma and non-glioblastoma slices to make pathology diagnosis more efficient and less labor intensive.
How to apply: If you are interested, please contact Prof. Dr Swaminathan a/l S Manickam via email at swaminathan@utar.edu.my