Master's thesis: Latent Functional Maps for Medical Imaging

23.10.2024, Diplomarbeiten, Bachelor- und Masterarbeiten

This project aims to use latent functional maps to align multimodal medical representations (e.g., text and vision). The first part of the thesis will involve a literature review on representation similarity. This will be followed by experimenting with the latent functional maps approach on a toy dataset of medical images and later applying it to real medical imaging tasks.

Neural Networks (NNs) learn to represent high-dimensional data as elements of lower-dimensional latent spaces. Modeling the relationships between these representational spaces is an ongoing challenge. Successfully addressing this challenge could enable the reuse of representations in downstream tasks, reducing the need to retrain similar models multiple times. These kinds of approaches have not yet been explored in the context of medical imaging datasets, where aligning multimodal representations could significantly enhance the effectiveness of models in medical applications.

This project aims to use latent functional maps to align multimodal medical representations (e.g., text and vision). The first part of the thesis will involve a literature review on representation similarity. This will be followed by experimenting with the latent functional maps approach on a toy dataset of medical images and later applying it to real medical imaging tasks.


Please send an email, including a CV, a current transcript of records and a brief statement on why you are interested in the project

Kontakt: maxime.di-folco@tum.de

More Information

https://compai-lab.github.io/files/Msc-functionalmaps.pdf