Dissertation for Applied AI for Medical Imaging

IIDS67590 Dissertation for Applied AI for Medical Imaging 60 Full year Postgraduate Taught Level 7 Division of Informatics, Imaging and Data Sciences 1 FHEQ level (Framework for Higher Education Qualifications) ' Masters/Integrated Masters P4 ' European Credit Transfer & Accumulation System Rating : 30.0 The dissertation provides students with an opportunity to undertake an independent research or development project in Applied AI for Medical Imaging, drawing on the taught elements of the programme (e.g., image acquisition and reconstruction, medical image analysis, deep learning, modelling/simulation, and translation to practice). Projects should address a well-defined problem in medical imaging and demonstrate an appropriate balance of technical depth, scientific rigour, and clinical or translational relevance. Students will consider the impact on healthcare and/or biomedical research, including issues of data governance, validation, safety, ethics, and the practical constraints of clinical deployment. This aligns with the programme’s overall aims around imaging, AI methods, biomarkers, modelling/simulation, and translation into practice.  The dissertation provides students with an opportunity to undertake an independent research or development project in Applied AI for Medical Imaging, drawing on the taught elements of the programme (e.g., image acquisition and reconstruction, medical image analysis, deep learning, modelling/simulation, and translation to practice). Projects should address a well-defined problem in medical imaging and demonstrate an appropriate balance of technical depth, scientific rigour, and clinical or translational relevance. Students will consider the impact on healthcare and/or biomedical research, including issues of data governance, validation, safety, ethics, and the practical constraints of clinical deployment. This aligns with the programme’s overall aims around imaging, AI methods, biomarkers, modelling/simulation, and translation into practice.  The unit aims to:The overall aim of this unit is for the student to undertake a substantial independent project that demonstrates originality in the application of AI to medical imaging, together with a practical understanding of how established research and engineering methods are used to create, evaluate, and communicate new knowledge or capability.  The aims of the dissertation are to:  <ol><li>Support the student in pursuing independent work on a specified topic in Applied AI for Medical Imaging.  </li><li>Develop research and development skills: problem formulation, experimental design, implementation, evaluation, and interpretation.  </li><li>Enable a concentrated and critical review of relevant literature in medical imaging, AI/ML, and modelling/simulation and translational science.  </li><li>Support students in applying knowledge and tools from the taught programme (e.g., Python-based ML, GPU training workflows, imaging data handling, validation concepts).  </li><li>Enable the student to demonstrate mastery of a specific area, including clear articulation of limitations, risks, and future work. </li></ol> Students should be able to: <ul><li>Demonstrate a critical understanding of research methodologies and techniques</li><li>Evaluate other perspectives in relation to their chosen topic </li></ul> Students should be able to: <ul><li>Evaluate critically the strengths and limitations of their own and others research</li><li>Draw (and justify) conclusions from the results and interpret findings for medical imaging and applied healthcare</li><li>Understand the impact of the research and recommend future direction of the research while recognising uncertainty and limitations in real-world data use</li><li>Show critical thinking capacity, including abstraction, analysis and critical judgement </li></ul> Students should be able to: <ul><li>Produce an extended piece of writing with a clear structure in an appropriate style and uses a conventional system of full and accurate referencing</li><li>Plan and undertake a research project that focuses on a specific area in health data science</li><li>Conduct a critical review of the literature and the status of research in the chosen field</li><li>Document, curate, and analyse medical imaging and associated clinical data using appropriate methods (e.g., statistical analysis and data manipulation), applying principles of reproducible and transparent research in applied AI for medical imaging.</li><li>Apply appropriate ethical, legal, and information governance principles in the use of medical data. </li></ul> Students should be able to: <ul><li>Engage in academic and professional communication with others</li><li>Show initiative and self-direction in academic and professional development</li><li>Undertake independent study and manage time appropriately</li><li>Use appropriate software for presentation of a professional academic report</li><li>Behave in a professional manner and follow professional conduct guidelines. </li></ul> Students will work under the guidance of a dissertation supervisor (or supervisory team). Supervision will normally include at least six formal meetings across the dissertation period, with additional contact as appropriate to the project type and stage. Meetings may be face-to-face or conducted via online tools approved by the University.  Support mechanisms typically include:  <ol><li>Supervisor guidance on project scoping, methodology, evaluation, and academic writing.  </li><li>Access to computing and data resources appropriate for AI and imaging work (e.g., managed GPU access and/or approved platforms as defined by the programme’s computing provision).  </li><li>Structured support from the dissertation team, which may cover: coding, experiments, reproducibility, statistics, scientific writing, and presentation preparation.  </li><li>Online spaces (e.g., Canvas/Teams) for announcements, guidance, and peer discussion.  </li></ol>Expected dissertation structure (typical): Introduction; Background/Literature Review; Methods; Experiments/Evaluation; Results; Discussion (including limitations, ethics/responsible AI considerations, and future work); Conclusion.  4 Dissertation 100% Formative assessment (throughout): Students are expected to provide regular progress updates (e.g., brief written updates, meeting notes, milestone check-ins). Supervisors provide ongoing formative feedback during meetings and via agreed communication channels. <ul><li>Meeting length varies</li><li>In person/Canvas/Teams</li></ul>Summative: Final dissertation submissionWritten report: An individual report detailing research work covering each of the elements of the marking breakdown (introduction, aims, methods, results and discussion). <ul><li>10,000 words maximum. </li><li>Submission before deadline</li><li>100% weighting within unit</li></ul> Marks and feedback returned up to 6 weeks after submission  MSc Applied AI for Med Imaging MSc Applied AI for Med Imaging Not Set Mandatory N As advised by supervisors 600