Multi-task UNet: Jointly Boosting Saliency Prediction and Disease Classification on Chest X-ray Images
This work addresses a specific problem for medical imaging researchers by providing an incremental improvement in multi-task learning for chest X-ray analysis.
The paper tackles the problem of data deficiency in visual saliency prediction on chest X-ray images by proposing a multi-task UNet model that jointly performs saliency prediction and disease classification, and it reports outperforming existing methods in both tasks.
Human visual attention has recently shown its distinct capability in boosting machine learning models. However, studies that aim to facilitate medical tasks with human visual attention are still scarce. To support the use of visual attention, this paper describes a novel deep learning model for visual saliency prediction on chest X-ray (CXR) images. To cope with data deficiency, we exploit the multi-task learning method and tackles disease classification on CXR simultaneously. For a more robust training process, we propose a further optimized multi-task learning scheme to better handle model overfitting. Experiments show our proposed deep learning model with our new learning scheme can outperform existing methods dedicated either for saliency prediction or image classification. The code used in this paper is available at https://github.com/hz-zhu/MT-UNet.