fusilli 0.0.7

Comparing multi-modal data fusion methods. Don't be silly, use Fusilli!

fusilli

🌸 Don't be silly, use fusilli for all your multi-modal data fusion needs! 🌸

Introduction

🍝 Welcome to fusilli 🍝, where we're cooking up something special in the world of machine learning! Fusilli is your go-to library for multi-modal data fusion methods, and it's designed to make data fusion a piece of cake! 🍰

What can Fusilli do?

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:

1. Tabular-Tabular Fusion: Combine two different types of tabular data.
2. 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.

Detailed Documentation

Want to know more? Here is a link to Read the Docs

• Detailed descriptions of the methods included in fusilli
• Examples and tutorials on how to use fusilli, with examples of:
  • Loading your own data
  • Logging experiments
  • Modifying model structures
• A recipe book of API documentation for the fusilli codebase.

Installation

To savour the flavours of fusilli, you can install it using pip:

pip install fusilli

How to Cite

Coming soon...

Contribute!

We'd love to add some more multi-modal data fusion methods if you've found any! PyTorch templates and contribution guidance our in the contributions documentation.

Authors and Acknowledgements

fusilli is authored by Florence J Townend, James Chapman, and James H Cole.

This work was funded by the EPSRC (Funding code to be added).