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Deep learning-driven image analysis of epithelial tissues structure and organization
While epithelial tissues are ubiquitous across the body, they vary greatly in how they are organised. Epithelial tissue ranges from highly organised single layers of cells to complex, multilayered tissues. Towards understanding the physical principles that give rise to these different phenotypes, we aim to study how these tissues are organised and how the cells are shaped.
Keywords: image analysis, deep learning, epithelia
In this project, the student will optimise and apply image analysis pipelines that have been established in the Iber Lab to study how the cellular organisation varies across different types of epithelial tissues. Towards this goal, the student will apply state of the art deep learning algorithms to light-sheet fluorescence microscopy images of epithelial tissue.
In this project, the student will optimise and apply image analysis pipelines that have been established in the Iber Lab to study how the cellular organisation varies across different types of epithelial tissues. Towards this goal, the student will apply state of the art deep learning algorithms to light-sheet fluorescence microscopy images of epithelial tissue.
We seek a student with previous experience in programming (e.g., python or MATLAB) for a 3 or 6 month project. Previous experience in deep learning and image analysis are helpful, but not required. The student will receive training in image analysis, deep learning, and data science best practices.
We seek a student with previous experience in programming (e.g., python or MATLAB) for a 3 or 6 month project. Previous experience in deep learning and image analysis are helpful, but not required. The student will receive training in image analysis, deep learning, and data science best practices.