Yes, I think this would work. For example, you'd organize the data into 3 tables: patients, behaviors and images. The patients table would have a partially filled-out "diagnosis" column. The model would then predict diagnosis of not-yet-diagnosed patients based on the patterns in data fields of previously diagnosed patients.
TabPFN is an amazing innovation. But there are some crucial differences in model capabilities that make it hard for a fair comparison.
TabPFN can only operate on a single small table. But real-world datasets are actually multi-table and to make accurate prediction you need to capture signal from multiple tables (for example, customers, products, purchases).
So, the comparison to TabPFN would be unfair as it would only use data from a single table and that would lead to bad performance of TabPFN.
