Partial Information Decomposition as a Multi-Contrast 3D MRI Selection Strategy for Resource-Constrained Deep Neural Network Training in Brain Tumor Segmentation

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Researchers applied Partial Information Decomposition to figure out which MRI sequences actually contribute unique data for tumor segmentation. By selecting only the most 'synergistic' pairs, they achieved nearly identical accuracy to using the full dataset while drastically reducing compute requirements. This proves we can often trade brute-force data for smarter feature selection in specialized 3D vision tasks.