🍝 Welcome to fusilli 🍝, the ultimate library for multi-modal data fusion in machine learning! Fusilli makes data
fusion a piece of cake, providing a platform to combine different data types efficiently.
Multi-modal data fusion is the combination of different types of data (or data modalities) in the pursuit of some common
goal. For example, using both blood test results and neuroimaging to predict whether somebody will develop a disease.
There are many different ways to combine data modalities, and the aim of fusilli is to provide a platform for
anybody to compare different methods against each other.
Fusilli is built using PyTorch Lightning and PyTorch Geometric, and it currently supports the following scenarios:
- Tabular-Tabular Fusion: Combine two different types of tabular data.
- Tabular-Image Fusion: Combine one type of tabular data with image data (2D or 3D).
Fusilli supports a range of prediction tasks, including regression, binary classification, and multi-class classification. Note that it does not currently support tasks such as clustering or segmentation.
Want to know more? Here is a link to Read the Docs
To savour the flavours of fusilli, you can install it using pip:
pip install fusilli
Coming soon...
If you've developed new fusion methods or want to enhance Fusilli, check our contribution guidelines to get started. PyTorch templates and contribution guidance our in the contributions documentation.
fusilli is authored by Florence J Townend, James Chapman, and James H Cole.
Florence J Townend is supported by a UCL UKRI Centre for Doctoral Training in AI-enabled Healthcare studentship ( EP/S021612/1).
This project is licensed under AGPLv3. See the LICENSE file for details.