MultiMix: Sparingly Supervised, Extreme Multitask Learning From Medical Images
This work addresses the challenge of leveraging unlabeled data for medical image analysis, offering a solution for scenarios with scarce annotations, though it appears incremental in combining semi-supervised and multitask learning approaches.
The authors tackled the problem of learning disease classification and anatomical segmentation from limited labeled medical images by proposing MultiMix, a multitask learning model that achieved effective pneumonia classification and lung segmentation from chest X-rays, with evaluations showing adaptability to generalization scenarios.
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.