Few-Shot Medical Segmentation Using Deep Learning

We are seeking a highly motivated and talented candidate for a self-funded PhD position in the field of Few-Shot Medical Segmentation using Deep Learning. This opportunity is ideal for individuals with a strong background in computer vision, machine learning, and a passion for advancing medical image analysis.Medical image segmentation plays a crucial role in disease diagnosis, treatment planning, and medical research. In this project, the selected PhD candidate will work on developing cutting-edge deep learning techniques for few-shot medical image segmentation. Few-shot learning enables models to accurately segment medical images with minimal annotated data, a critical aspect in the medical field where data acquisition and labeling can be expensive and time-consuming.The project will involve:- Investigating state-of-the-art few-shot learning algorithms and adapting them to the medical imaging domain.- Developing novel neural network architectures for robust and accurate medical image segmentation..- Evaluating and benchmarking the developed methods on diverse medical imaging datasets.This research project offers a unique opportunity to make a significant impact on the healthcare industry by advancing the state-of-the-art in medical image analysis.

Brunel offers a number of funding options to research students that help cover the cost of their tuition fees, contribute to living expenses or both. See more information here: https://www.brunel.ac.uk/research/Research-degrees/Research-degree-funding. The UK Government is also offering Doctoral Student Loans for eligible students, and there is some funding available through the Research Councils. Many of our international students benefit from funding provided by their governments or employers. Brunel alumni enjoy tuition fee discounts of 15%.