PhD Studentship in AI-based Medical Image Analysis for Early Detection and Diagnosis
PhD Call for Applications at VinUni and University of Technology Sydney
AI-based Medical Image Analysis for Early Detection and Diagnosis
Open date: 15/12/2022 – Closing date: 15/01/2023
Medical image analysis is an active research field where advanced image analysis solutions are developed to predict, diagnose or monitor diseases. It is in high demand due to its better precision compared with conventional diagnosis in many scenarios. In particular, many tasks in medical image analysis have achieved human-level performance with the support of advanced machine learning and computer vision techniques. However, the development of effective AI-based solutions for medical image analysis and putting them into everyday clinical use, however, is challenging due to the lack of large, annotated datasets and explainability ability. This thesis focuses on developing and testing image analysis algorithms based on advanced machine learning techniques to solve a number of very important yet challenging medical image analysis problems. This includes (but is not limited to) the early diagnosis of common diseases (e.g., abnormal findings from chest X-ray scans, breast cancers from mammograms), spinal lesions detection and classification (on spine X-rays), and brain lesions detection and segmentation on CT and MRI scans (for brain cancer staging), etc. Furthermore, it will investigate deep learning solutions for lesion detection by exploring compact feature representation learning, graph model, domain adaptation, attention model, self-supervised learning, knowledge distillation, and sequence learning. We also aim to investigate alternative techniques to train AI systems with less annotated data (e.g., exploring large-scale, un-annotated data, and few-shot learning) and make them more explainable (Explainable AI).
We are looking for Ph.D. students to join our research team and to build safe and high-precision computational tools for improving image-based detection and diagnosis of disease. We are passionate about applying Computer Vision (CV), Machine Learning (ML), and Deep Learning (DL) models to build computed-aided detection (CAD) and computer-aided diagnosis (CADx) systems from very large-scale clinical datasets of multiple imaging modalities (X-ray, CT, MRI, etc).
As a Ph.D. student, you will undertake training courses that will lead towards a Ph.D. and allow you to gain various skills and expertise to strongly support your future career, whether in industry or academia. Students will be supported in publishing their research and encouraged to present it at international conferences. This thesis shall be supervised by Dr. Hieu Pham from the College of Engineering and Computer Science (CECS), VinUniversity and Prof. Qiang Wu, University of Technology Sydney (UTS) and Prof. Min Xu, University of Technology Sydney (UTS). The student will benefit from weekly seminars and daily interactions with Research Scientists and Biomedical Researchers at VinUni-Illinois Smart Health Center (VISHC).
This project is suitable for students (Vietnamese only) with a Bachelor’s degree in mathematics, computer science, bioinformatics, or a related field. Academic excellence should be demonstrated, i.e. GPA of 3.2 or above. A Master’s degree and/or experience in image analysis or AI/ML methods are desirable but not essential. Applicants should have strong mathematical and machine learning knowledge. Excellent prototyping skills in Python; knowledge and development experience of common deep learning frameworks and packages (PyTorch, TensorFlow, Keras, etc.). The key requirements are an interest in the topic and a good work ethic. English language requirements: IELTS Academic: 6.5 or above (or equivalent).
Selected candidates will receive a scholarship of 100% tuition fee (40,000 USD/year) and a stipend of 10,000 USD/year. You will also have the opportunity to conduct research for 1 to 2 years at the University of Technology, Sydney (UTS) under the 1.100 scholarship program of Vingroup. This program will cover all tuition fees, living expenses, health insurance, round-trip airfare, and related expenses for qualified Ph.D. students.
How to apply
To arrange an informal discussion, please email Dr. Hieu Pham (firstname.lastname@example.org) and copy Prof. Qiang Wu (email@example.com). You must include your CV, a research summary, and the names of two recommenders who can write letters on their behalf.