Semi-Supervised Learning with Spatial Pseudo Labeling for Peritumoral Infiltration Prediction in Glioblastoma using MR Fingerprinting

Existing glioblastoma (GB) infiltration models are often limited by lack of true infiltration labels and employ the assumption that edema closer to tumor has higher infiltrative potential relative to distant edema. Here, we propose a semi-supervised learning scheme that incorporates pretraining on the near-far heuristic and spatial pseudo labeling using true infiltration labels for voxel-wise tumor infiltration prediction. Our results show improved classification performance following finetuning on labeled infiltration data compared to training on the near-far heuristic alone and indicate the potential in employing MR fingerprinting-based models to guide GB diagnosis and treatment.