# Semi-Supervised Multi-Task Learning With Chest X-Ray Images

## TL;DR

This paper introduces a semi-supervised multi-task learning approach for chest X-ray images, combining classification and segmentation with a novel loss function to improve convergence and performance.

## Contribution

It proposes a new multi-task model with a combined loss function (KLTV) and a novel segmentation architecture (APPAU-Net) for better semi-supervised learning in medical imaging.

## Key findings

- KLTV loss accelerates convergence and enhances segmentation accuracy.
- The proposed model improves multi-task learning performance with limited labeled data.
- APPAU-Net demonstrates superior segmentation results in experiments.

## Abstract

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.