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Multi-task UNet: Jointly Boosting Saliency Prediction and Disease Classification on Chest X-ray Images

2022-02-15 01:12:42
Hongzhi Zhu, Robert Rohling, Septimiu Salcudean

Abstract

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 this https URL.

Abstract (translated)

URL

https://arxiv.org/abs/2202.07118

PDF

https://arxiv.org/pdf/2202.07118.pdf


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