| Departments |
Computer Science, Zienkiewicz Institute for Modelling, Data and AI
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| Description | This project introduces a novel approach to mapping dynamic interactions in networks of human cardiac cells, focusing on the causes of network dyssynchronisation, a key factor in heart rhythm failure. By employing machine learning (ML) techniques, the project aims to quantify cell-to-cell interactions using datasets and videos from human cellular networks under various conditions. The innovative ML framework will help extract and visualize data, predict behaviours, and provide deeper insights into the spatial and temporal aspects of cardiomyocyte networks. Ultimately, this research seeks to improve understanding of early heart dysfunction and guide better diagnosis and treatment.
BSc level: the project will focus on processing available data (.e.g., simple feature reduction algorithms and predictions) or/and performing segmentation tasks with algorithms found in the literature by the student.
MSc level: same as BSc level, but with more emphasis on the segmentation aspects (including data preparation, fine-tuning models, etc.) and profiling segmented cells.
PhD level: the candidate will aim at producing AI pipelines for multiclass segmentation and/or cascade of multiple segmentation+classification steps to detect single cells and aggregates, extract calcium signals and use the obtained information to advance knowledge in biology. To achieve this interdisciplinary goal, the student will interact with members of the Molecular Cardiology Group. |
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| Preparation | It is suggested to read:
Caraffini, F., Eshkiki, H., Mohammadpour, M., Sullo, N., George, C.H. (2024). Towards Improving Single-Cell Segmentation in Heterogeneous Configurations of Cardiomyocyte Networks. In: Xie, X., Styles, I., Powathil, G., Ceccarelli, M. (eds) Artificial Intelligence in Healthcare. AIiH 2024. Lecture Notes in Computer Science, vol 14976. Springer, Cham. https://doi.org/10.1007/978-3-031-67285-9_8 |
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| Project Categories |
Artificial Intelligence (AI), Data Science |
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| Project Keywords |
Algorithms, Data Visualisation, Health Informatics, Machine Learning |
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Level of Studies
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| Level 6 (Undergraduate Year 3) |
yes
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| Level 7 (Masters) |
yes
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| Level 8 (PhD) |
yes
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