  Semi-supervised learning via learning from limited quantities of labeled data
has been investigated as an alternative to supervised counterparts. Maximizing
knowledge gains from copious unlabeled data benefit semi-supervised learning
settings. Moreover, learning multiple tasks within the same model further
improves model generalizability. We propose a novel multitask learning model,
namely MultiMix, which jointly learns disease classification and anatomical
segmentation in a sparingly supervised manner, while preserving explainability
through bridge saliency between the two tasks. Our extensive experimentation
with varied quantities of labeled data in the training sets justify the
effectiveness of our multitasking model for the classification of pneumonia and
segmentation of lungs from chest X-ray images. Moreover, both in-domain and
cross-domain evaluations across the tasks further showcase the potential of our
model to adapt to challenging generalization scenarios.
