  We propose a fully 3D multi-path convolutional network to predict stroke
lesions from 3D brain MRI images. Our multi-path model has independent encoders
for different modalities containing residual convolutional blocks, weighted
multi-path feature fusion from different modalities, and weighted fusion
modules to combine encoder and decoder features. Compared to existing 3D CNNs
like DeepMedic, 3D U-Net, and AnatomyNet, our networks achieves the highest
statistically significant cross-validation accuracy of 60.5% on the large ATLAS
benchmark of 220 patients. We also test our model on multi-modal images from
the Kessler Foundation and Medical College Wisconsin and achieve a
statistically significant cross-validation accuracy of 65%, significantly
outperforming the multi-modal 3D U-Net and DeepMedic. Overall our model offers
a principled, extensible multi-path approach that outperforms multi-channel
alternatives and achieves high Dice accuracies on existing benchmarks.
