Multi-task UNet: Jointly Boosting Saliency Prediction and Disease Classification on Chest X-ray Images
Keywords: Saliency Prediction, Disease Classification, X-ray Imaging, Deep Learning, Multi-task learning
TL;DR: A novel multi-task learning model with optimized training scheme that boosts saliency prediction and disease classification simultaneously on chest X-ray images.
Abstract: Human visual attention has recently shown its distinct capability in boosting machine learning models. However, studies that aim to facilitate medical tasks with 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.
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Paper Type: both
Primary Subject Area: Transfer Learning and Domain Adaptation
Secondary Subject Area: Learning with Noisy Labels and Limited Data
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Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2202.07118/code)
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