Proceedings

KG-MultiMS: a Knowledge Graph-Enhanced Multimodal Framework for Multiple Sclerosis

Riccardo Francia

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Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence. Doctoral Consortium. Pages 8329-8330.

https://doi.org/10.24963/ijcai.2026/943

Keywords

  • Machine Learning: Knowledge-aided learning
  • Computer Vision: Biomedical image analysis
  • Machine Learning: Multi-modal learning
  • Data Mining: Knowledge graphs and knowledge base completion

Abstract

Despite advances in the computational analysis of multiple sclerosis, the ability to accurately predict disease progression remains limited due to unimodal approaches in the context of deep learning. KG-MultiMS proposes as a multimodal deep-learning framework that integrates data from three heterogeneous sources: (1) MRI volumes using the Swin-UNETR architecture to learn both semantic embeddings and segmentation masks of brain lesions; (2) tabular clinical variables using the FT-Tokenizer architecture to learn clinical embeddings; (3) medical knowledge graph based on the Harvard-Oxford atlas using a Heterogeneous Graph Transformer to learn contextual embeddings from patient-specific subgraphs. A cross-attention mechanism will allow for leveraging multimodal contributions by dynamically weighting them based on the anatomical-functional concordance represented in the contextual embeddings.

Riccardo Francia. 2026. KG-MultiMS: a Knowledge Graph-Enhanced Multimodal Framework for Multiple Sclerosis. In Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-26. IJCAI, 8329–8330. https://doi.org/10.24963/ijcai.2026/943
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