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  Few-Shot Medical Segmentation Using Deep Learning


   Electronic and Electrical Engineering

   Applications accepted all year round  Self-Funded PhD Students Only

About the Project

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.


Computer Science (8)

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