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Background:

Echocardiography is a non-invasive imaging technique that uses ultrasound waves to produce images of the heart. It is a widely used diagnostic tool for a variety of cardiovascular conditions, including heart failure, valvular heart disease, and congenital heart disease.

Echocardiographic images are complex and contain a wealth of information about the structure and function of the heart. However, manually extracting this information from echocardiographic images is a time-consuming and challenging task.

Machine learning has the potential to automate the analysis of echocardiographic images and make them more accessible to clinicians. In particular, deep learning-based encoders can be used to extract informative features from echocardiographic images that can be used for a variety of tasks, such as disease classification, segmentation, and quantification.

Traditional deep learning-based encoders are trained using supervised learning, which requires a large dataset of labelled echocardiographic images. However, such datasets are expensive and time-consuming to collect.

Self-supervised learning is a promising alternative to supervised learning for training deep learning-based encoders. Self-supervised learning does not require labelled data, and instead learns features from unlabeled data by solving pretext tasks.

Research Questions:

This PhD project will explore the following research questions:

- Can self-supervised learning be used to develop echocardiographic encoders that are more efficient and effective than traditional encoders trained on labelled data?

- What are the best self-supervised learning architectures and training strategies for echocardiographic image analysis?

- How can self-supervised learning encoders be used to develop clinically useful applications, such as automated image segmentation and classification?

Aim and Objectives:

The aim of this PhD project is to develop self-supervised learning encoders for echocardiographic image analysis. The specific objectives are to:

- Develop self-supervised learning synthetic tasks tailored for training echocardiographic encoders without explicit labels.

- Optimise encoder architectures to extract clinically relevant features from unlabeled echocardiographic data.

- Train the self-supervised learning encoders on a large dataset of unlabeled echocardiographic images.

- Evaluate the transferability of learned representations for downstream clinical tasks, including classification and segmentation.

Methodology:

The aim of this PhD project is to develop self-supervised learning encoders for echocardiographic image analysis. The specific objectives are to:

- Develop self-supervised learning synthetic tasks tailored for training echocardiographic encoders without explicit labels.

- Optimise encoder architectures to extract clinically relevant features from unlabeled echocardiographic data.

- Train the self-supervised learning encoders on a large dataset of unlabeled echocardiographic images.

- Evaluate the transferability of learned representations for downstream clinical tasks, including classification and segmentation.

Clinical Partners:

Collaboration with clinical partners at the School of Medicine, Imperial College London, involves regular meetings and discussions to:

- Ensure alignment with clinical needs and priorities.

- Validate the clinical relevance of the developed encoders.

- Facilitate the translation of research findings into practical applications.

- Availability of patient data and expert annotationtations for model developments.

For more information about the project, please contact Professor Massoud Zolgharni