Semi-Supervised Multi-Task Learning With Chest X-Ray Images
This work addresses the challenge of data scarcity in medical imaging for healthcare applications, though it is incremental as it builds on existing generative and multi-task learning methods.
The paper tackles the problem of limited labeled data in medical imaging by proposing a semi-supervised multi-task learning model that jointly learns classification and segmentation from chest X-ray images, achieving faster convergence and competitive segmentation performance with a new KLTV loss function.
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.