We propose a compositional graph-based Machine Learning (ML) framework for Alzheimer’s disease (AD) detection that constructs complex ML predictors from modular components. In our directed computational graph, datasets are represented as nodes \(n_i\) , and deep learning (DL) models are represented as directed edges \(n_i \rightarrow n_j\) , allowing us to model complex image-processing pipelines \(n_1 \rightarrow n_2 \rightarrow n_3... \rightarrow n_T\) as end-to-end DL predictors. Each directed path in the graph functions as a DL predictor, supporting both forward propagation for transforming data representations, as well as backpropagation for model finetuning, saliency map computation, and input data optimization. We demonstrate our model on Alzheimer’s disease prediction, a complex problem that requires integrating multimodal data containing scans of different modalities and contrasts, genetic data and cognitive tests. We built a graph of 11 nodes (data) and 14 edges (ML models), where each model has been trained on handling a specific task (e.g. skull-stripping MRI scans, AD detection,image2image translation, ...). By using a modular and adaptive approach, our framework effectively integrates diverse data types, handles distribution shifts, and scales to arbitrary complexity, offering a practical tool that remains accurate even when modalities are missing for advancing Alzheimer’s disease diagnosis and potentially other complex medical prediction tasks.
