Figure 6.

Proposed PreSANet deep learning architecture for radiotherapy outcome prediction. The convolutional backbones consist of the combination of the preprocessor module and the self-attention CNN feature extractor. Using shared weights, each of the two channel (PET, CT) input volume is split into individual slices passed to the backbone. The resulting per-slice feature vectors are aggregated using mean and variance into two 256-feature vectors. They are then concatenated with the outputs of a fully connected network component (yellow) that processes the clinical input data forming a 768 feature vector. Figure created with Draw.io