A multi-modal graph-based framework for Alzheimer’s disease detection
Abstract 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 rep...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-05966-2 |
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| Summary: | Abstract 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. |
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| ISSN: | 2045-2322 |