Fig. 1.

Modified U-Net architecture for multi-task learning. The segmentation and regression tasks are split at the final up-sampling and concatenation layer. The final feature map in the segmentation path is passed through a sigmoid layer to obtain a per-pixel image segmentation. Similarly, the final feature map in the regression path is down-sampled (by max-pooling) to 1/4^th of its size and fed to a fully-connected layer to generate a single regression output. The logarithm of the task uncertainties (logσ₁, logσ₂) are set as network parameters and are encoded in the loss function (5), hence learned during the training.