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          <h1>Developing Echocardiographic Encoders Using Self-Supervised Learning</h1>
          <h2><i>University of West London – School of Computing and Engineering<i></h2>
          <h3>In Collaboration with the <a href="https://www.imperial.ac.uk/nhli/" target="_blank"
              rel="noopener noreferrer">National Heart and Lung Institute</a>
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            <h1> <b> Supervisory Team </b></h1>
            <h6> <a href="https://www.uwl.ac.uk/staff/massoud-zolgharni" target="_blank" rel="noopener noreferrer">
                Professor Massoud Zolgharni </a></h6>
            <h6> <a href="https://www.imperial.ac.uk/people/d.francis" target="_blank" rel="noopener noreferrer">
                Professor Darrel Francis </a></h6>

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                <h1>Background: </h1>
                <h3>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.</h3>
                <h3>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. </h3>
                <h3>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. </h3>
                <h3>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. </h3>
                <h3>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. </h3>


                <h1>Research Questions: </h1>
                <h3>This PhD project will explore the following research questions:</h3>
                <h3>- Can self-supervised learning be used to develop echocardiographic encoders that are more efficient
                  and effective than traditional encoders trained on labelled data? </h3>
                <h3>- What are the best self-supervised learning architectures and training strategies for
                  echocardiographic image analysis? </h3>
                <h3>- How can self-supervised learning encoders be used to develop clinically useful applications, such
                  as automated image segmentation and classification? </h3>


                <h1>Aim and Objectives: </h1>
                <h3>The aim of this PhD project is to develop self-supervised learning encoders for echocardiographic
                  image analysis. The specific objectives are to:</h3>
                <h3>- Develop self-supervised learning synthetic tasks tailored for training echocardiographic encoders
                  without explicit labels. </h3>
                <h3>- Optimise encoder architectures to extract clinically relevant features from unlabeled
                  echocardiographic data. </h3>
                <h3>- Train the self-supervised learning encoders on a large dataset of unlabeled echocardiographic
                  images. </h3>
                <h3>- Evaluate the transferability of learned representations for downstream clinical tasks, including
                  classification and segmentation. </h3>


                <h1>Methodology: </h1>
                <h3>The aim of this PhD project is to develop self-supervised learning encoders for echocardiographic
                  image analysis. The specific objectives are to:</h3>
                <h3>- Develop self-supervised learning synthetic tasks tailored for training echocardiographic encoders
                  without explicit labels. </h3>
                <h3>- Optimise encoder architectures to extract clinically relevant features from unlabeled
                  echocardiographic data. </h3>
                <h3>- Train the self-supervised learning encoders on a large dataset of unlabeled echocardiographic
                  images. </h3>
                <h3>- Evaluate the transferability of learned representations for downstream clinical tasks, including
                  classification and segmentation. </h3>


                <h1>Clinical Partners: </h1>
                <h3>Collaboration with clinical partners at the School of Medicine, Imperial College London, involves
                  regular meetings and discussions to:</h3>
                <h3>- Ensure alignment with clinical needs and priorities. </h3>
                <h3>- Validate the clinical relevance of the developed encoders. </h3>
                <h3>- Facilitate the translation of research findings into practical applications. </h3>
                <h3>- Availability of patient data and expert annotationtations for model developments. </h3>


                <br>
                <h3>For more information about the project, please contact <a
                    href="https://www.uwl.ac.uk/staff/massoud-zolgharni" target="_blank"
                    rel="noopener noreferrer">Professor Massoud Zolgharni</a> </h3>
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              C/O Professor Massoud Zolgharni <br>
              University of West London<br>
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              London, England.<br>
              W5 5RF<br><br>
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