Today, handling diverse data types like images, tables, or text is the norm in our data-driven world. However, combining these varied data sets to extract insights can be challenging. This is especially true for those working with MRI scans and clinical data to predict health outcomes.
Existing methods for combining different data types into a single predictive model can be complex and overwhelming. People sometimes have difficulty understanding the multitude of techniques available or implementing them efficiently.
Fusilli, a Python library, is designed to simplify the process of combining different data modalities. It offers fusion methods that make it easy to integrate varied data types for tasks like regression and classification. For example, whether predicting age based on brain MRI, blood test results, or questionnaire data, Fusilli provides a platform to combine these diverse data sources effectively.
The Capabilities of Fusilli
Fusilli simplifies the process of combining diverse data types, making exploration of different fusion models efficient. Its support for various fusion scenarios and predictive tasks makes it a valuable asset for extracting insights and predictions from various data sources.