Extended Data Fig. 10: Deep learning system diagram.

Flow chart of the deep learning system including ensembling and TTA. Model inputs are shaped as trapezoids. Deep learning networks are shaped as rectangles. Model outputs are shaped as pointed rectangles. a, The segmentation network takes a raw OCT scan as input to generate a dense segmentation of the OCT which is then fed into a Diagnosis and referral network to obtain auxiliary task referral and diagnosis labels. b, The auxiliary labels along with either the raw OCT scan or dense segmentation are inputted into each exAMD prediction network across each cross validation fold group. Although the arrows apply to one fold group and instance, they generalise across all fold groups and instances. c, Ten TTA predictions are obtained from each instance. All TTA predictions are combined via averaging to obtain the final ensembled prediction. d, Architecture of a single block in our network. Green circles are convolution layers applied sequentially to the input of the previous layer. Each convolution has stride 1 and uses ReLU activation. Four convolutions are shown for demonstrative purposes but the number of convolutions and the kernels used for each will differ between blocks. Each convolution has a skip connection to the last orange node which concatenates all the intermediate and final activations along the channel dimension as the output.