# Multi-task UNet: Jointly Boosting Saliency Prediction and Disease Classification on Chest X-ray Images Source: [Multi-task UNet: Jointly Boosting Saliency Prediction and Disease Classification on Chest X-ray Images]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:58:04.948Z Authors: Hongzhi Zhu, Robert Rohling, Septimiu Salcudean Published: 2022-02-15T01:12:42.000Z ## 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 https://github.com/hz-zhu/MT-UNet. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()