Self-pruning Graph Neural Network for Predicting Inflammatory Disease
  Activity in Multiple Sclerosis from Brain MR Images
Chinmay Prabhakar Hongwei Bran Li Johannes C. Paetzold Timo Loehr Chen Niu Mark Mühlau Daniel Rueckert Benedikt Wiestler Bjoern Menze
Multiple Sclerosis (MS) is a severe neurological disease characterized by
inflammatory lesions in the central nervous system. Hence, predicting
inflammatory disease activity is crucial for disease assessment and treatment.
However, MS lesions can occur throughout the brain and vary in shape, size and
total count among patients. The high variance in lesion load and locations
makes it challenging for machine learning methods to learn a globally effective
representation of whole-brain MRI scans to assess and predict disease.
Technically it is non-trivial to incorporate essential biomarkers such as
lesion load or spatial proximity. Our work represents the first attempt to
utilize graph neural networks (GNN) to aggregate these biomarkers for a novel
global representation. We propose a two-stage MS inflammatory disease activity
prediction approach. First, a 3D segmentation network detects lesions, and a
self-supervised algorithm extracts their image features. Second, the detected
lesions are used to build a patient graph. The lesions act as nodes in the
graph and are initialized with image features extracted in the first stage.
Finally, the lesions are connected based on their spatial proximity and the
inflammatory disease activity prediction is formulated as a graph
classification task. Furthermore, we propose a self-pruning strategy to
auto-select the most critical lesions for prediction. Our proposed method
outperforms the existing baseline by a large margin (AUCs of 0.67 vs. 0.61 and
0.66 vs. 0.60 for one-year and two-year inflammatory disease activity,
respectively). Finally, our proposed method enjoys inherent explainability by
assigning an importance score to each lesion for the overall prediction. Code
is available at https://github.com/chinmay5/ms_ida.git