Federated learning for tackling multimodal and class-imbalance problems in healthcare

The project aims to develop generalisable models in a distributed Federated Learning framework by investigating novel ways for boosting model performance, improving generalisability, tackling class imbalance, and addressing potential biases in data at client locations. The selected candidate is expected to work in a multi-disciplinary team, for example, with existing clinical collaborators.

<p style="text-align:justify"><strong>Background</strong></p> <p>Advancements in data-driven machine learning (ML) techniques have shown tremendous potential in various sectors, including healthcare. However, training a machine learning model and its deployment still needs to be improved as it is challenging in healthcare due to significant data heterogeneity and lack of access to big data. Data sharing between different sites is intractable due to privacy concerns and regulatory challenges. Most ML techniques are data-voracious and require large datasets to generalise on a particular population or data centre distribution. It is often not feasible to obtain large heterogeneous labelled datasets as obtaining ground truth labels is tedious and time-consuming, requiring expert time, which is expensive. As a result, most centres usually have small local datasets that are insufficient to train a model with high accuracy and good generalisability. Furthermore, data from a specific centre can only be biased to a particular population.</p> <p>Federated learning (FL) allows to train a model across decentralised devices or servers holding data locally which safeguards privacy and data security at the same time aiming to leverage the data from other centres in a distributed way. This increases the training dataset size and hence tackles the above-mentioned limitations in the medical domain. Even though several techniques in FL have been proposed in the past, utilising multi-modal data (both images with various modalities, and text) for multi-task learning (e.g., detection and diagnosis) has been limited and widely steered around model aggregation/fusion technique and fine tuning at client locations locally to avoid risk of data exposure. ML models trained in FL setting can still suffer from performance gap between seen and unseen patient settings and modality differences [1-2].</p> <p><strong>Objectives</strong></p> <p>The project aims to develop generalisable models in a distributed FL framework by investigating novel ways for boosting model performance, improving generalisability, tackling class imbalance, and addressing potential biases in data at client locations.</p> <p>A few multimodal datasets will be provided at the start of the project. Tasks of the project is purely research that lies on two fundamental questions – 1) can we leverage multi-modal data from various centres to enhance FL performance and its generalisability? and 2) can we devise a technique to uplift local performance, tackle local class-imbalance, and provide information regarding biases from the distributed data provided during the training?</p> <p><strong>References</strong></p> <p>[1] Q. Liu, C. Chen, J. Qin, Q. Dou and P. Heng, "FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space," in 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA, 2021 pp. 1013-1023. <a href="https://doi.org/10.1109/CVPR46437.2021.0010y">https://doi.org/10.1109/CVPR46437.2021.0010y</a></p> <p>[2] Subedi, R., Gaire, R.R., Ali, S., Nguyen, A., Stoyanov, D., Bhattarai, B. (2023). A Client-Server Deep Federated Learning for Cross-Domain Surgical Image Segmentation. Data Engineering in Medical Imaging. DEMI 2023. Lecture Notes in Computer Science, vol 14314. Springer, Cham. <a href="https://doi.org/10.1007/978-3-031-44992-5_3">https://doi.org/10.1007/978-3-031-44992-5_3</a></p> <p>[3] S. Ali, D. Jha, N. Ghatwary, S. Realdon, R. Cannizzaro, O.E. Salem, D. Lamarque, C. Daul, M.A. Riegler, K.V. Anonsen, A. Petlund. A multi-centre polyp detection and segmentation dataset for generalisability assessment. Scientific Data. 2023;10(1):75. <a href="https://doi.org/10.1038/s41597-023-01981-y">https://doi.org/10.1038/s41597-023-01981-y</a>   </p>

<p style="text-align:start; margin-bottom:24px">Formal applications for research degree study should be made online through the <a href="https://www.leeds.ac.uk/research-applying/doc/applying-research-degrees">University's website</a>. Please state clearly in the Planned Course of Study section that you are applying for <em><strong>PHD Computer Science FT</strong></em>, in the research information section that the research degree you wish to be considered for is <em><strong>Federated learning for tackling multimodal and class-imbalance problems in healthcare</strong></em> as well as <a href="https://eps.leeds.ac.uk/computing/staff/11465/dr-sharib-ali">Dr Sharib Ali</a> as your proposed supervisor and <em><strong>in the finance section, please state clearly the funding source that you are applying for, if you are self-funding or externally sponsored.</strong></em></p> <p>If English is not your first language, you must provide evidence that you meet the University's minimum English language requirements (below).</p> <p>As an international research-intensive university, we welcome students from all walks of life and from across the world. We foster an inclusive environment where all can flourish and prosper, and we are proud of our strong commitment to student education. Across all Faculties we are dedicated to diversifying our community and we welcome the unique contributions that individuals can bring, and particularly encourage applications from, but not limited to Black, Asian, people who belong to a minority ethnic community, people who identify as LGBT+ and people with disabilities. Applicants will always be selected based on merit and ability.</p> <p><em><strong>Please note that you must provide the following documents in support of your application by the closing date of Monday 6 January 2025 if applying for the China Scholarship Council-University of Leeds Scholarship, Monday 3 February 2025 if applying for Leeds Doctoral Scholarship or Tuesday 1 April 2025 for Leeds Opportunity Research Scholarship.</strong></em></p> <p><em><strong>If you are applying with external sponsorship or you are funding your own study, please ensure you provide your supporting documents at the point you submit your application:</strong></em></p> <ul> <li>Full Transcripts of all degree study or if in final year of study, full transcripts to date</li> <li>Personal Statement outlining your interest in the project</li> <li>CV</li> </ul>

Applicants to research degree programmes should normally have at least a first class or an upper second class British Bachelors Honours degree (or equivalent) in an appropriate discipline. The criteria for entry for some research degrees may be higher, for example, several faculties, also require a Masters degree. Applicants are advised to check with the relevant School prior to making an application. Applicants who are uncertain about the requirements for a particular research degree are advised to contact the School or Graduate School prior to making an application.

The minimum English language entry requirement for research postgraduate research study in the School of Computer Science is an IELTS of 6.5 overall with at least 6.5 in writing and at least 6.0 in reading, listening and speaking or equivalent. The test must be dated within two years of the start date of the course in order to be valid.

<p><strong>Self-Funded or externally sponsored students are welcome to apply.</strong></p> <p><strong>UK</strong> – The <a href="https://phd.leeds.ac.uk/funding/138-leeds-doctoral-scholarship-2025-faculty-of-engineering-and-physical-sciences#:~:text=Key%20facts&text=One%20Leeds%20Doctoral%20Scholarship%20is,rata%20for%20part%2Dtime%20study.">Leeds Doctoral Scholarship</a> <strong>(closing date: Monday 3 February 2025)</strong> and <a href="https://phd.leeds.ac.uk/funding/234-leeds-opportunity-research-scholarship-2022">Leeds Opportunity Research Scholarship</a> <strong>(closing date: Tuesday 1 April 2025)</strong> are available to UK applicants.  <a href="https://phd.leeds.ac.uk/funding/60-alumni-bursary">Alumni Bursary</a> is available to graduates of the University of Leeds.</p> <p><strong>Non-UK</strong> – The <a href="https://phd.leeds.ac.uk/funding/48-china-scholarship-council-university-of-leeds-scholarships-2021">China Scholarship Council - University of Leeds Scholarship</a> is available to nationals of China <strong>(closing date: Monday 6 January 2025)</strong>. The <a href="https://phd.leeds.ac.uk/funding/73-leeds-marshall-scholarship">Leeds Marshall Scholarship</a> is available to support US citizens. <a href="https://phd.leeds.ac.uk/funding/60-alumni-bursary">Alumni Bursary</a> is available to graduates of the University of Leeds.</p> <p>You will be responsible for paying the overtime fee in full in your writing up/overtime year (£320 in Session 2024/25), but the scholarship maintenance allowance will continue to be paid for up to 6 months in the final year of award.</p> <p><strong>Important:</strong> Please note that that the award does <em><strong>not</strong></em> cover the costs associated with moving to the UK.  All such costs (<a href="https://www.leeds.ac.uk/international-visas-immigration/doc/applying-student-visa">visa, Immigration Health Surcharge</a>, flights etc) would have to be met by yourself, or you will need to find an alternative funding source. </p> <p>Please refer to the <a href="https://www.ukcisa.org.uk/">UKCISA</a> website for information regarding Fee Status for Non-UK Nationals.</p>

<p>For information about this project, please contact Dr Sharib Ali by email to <a href="mailto:EMAIL@leeds.ac.uk">s.s.ali@leeds.ac.uk</a></p> <p>For further information about your application, please contact PGR Admissions by email to <a href="mailto:phd@engineering.leeds.ac.uk">phd@engineering.leeds.ac.uk</a></p>