  Discriminative models that require full supervision are inefficacious in the
medical imaging domain when large labeled datasets are unavailable. By
contrast, generative modeling---i.e., learning data generation and
classification---facilitates semi-supervised training with limited labeled
data. Moreover, generative modeling can be advantageous in accomplishing
multiple objectives for better generalization. We propose a novel multi-task
learning model for jointly learning a classifier and a segmentor, from chest
X-ray images, through semi-supervised learning. In addition, we propose a new
loss function that combines absolute KL divergence with Tversky loss (KLTV) to
yield faster convergence and better segmentation performance. Based on our
experimental results using a novel segmentation model, an Adversarial Pyramid
Progressive Attention U-Net (APPAU-Net), we hypothesize that KLTV can be more
effective for generalizing multi-tasking models while being competitive in
segmentation-only tasks.
