mlsanityData to 14 Oct 2026

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

arXiv 2202.07118 ↗First posted 15 Feb 2022eess.IV cs.CVcited by 4

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 36 of its 65 references that are in the map. First posted on arXiv 15 Feb 2022.

Builds on

36 of 65 references.

References · 36

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  4. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  5. 2016Densely Connected Convolutional Networkscited by 6,720
  6. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
  7. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 5,045
  8. 2016Feature Pyramid Networks for Object Detectioncited by 5,036
  9. 2015Learning Deep Features for Discriminative Localizationcited by 2,436
  10. 2013Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Mapscited by 2,371
  11. 2016Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learningcited by 1,512
  12. 2019CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparisoncited by 1,423
24 more
  1. 2017Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semanticscited by 1,358
  2. 2021EfficientNetV2: Smaller Models and Faster Trainingcited by 1,135
  3. 2017ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseasescited by 972
  4. 2017A Survey on Multi-Task Learningcited by 840
  5. 2017GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networkscited by 834
  6. 2018Multi-Task Learning as Multi-Objective Optimizationcited by 821
  7. 2019MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographscited by 597
  8. 2020Multi-Task Learning with Deep Neural Networks: A Surveycited by 375
  9. 2019What Makes Training Multi-Modal Classification Networks Hard?cited by 344
  10. 2018A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decaycited by 226
  11. 2019Causality matters in medical imagingcited by 193
  12. 2016What do different evaluation metrics tell us about saliency models?cited by 185
  13. 2016Grad-CAM: Why did you say that?cited by 161
  14. 2018Auxiliary Tasks in Multi-task Learningcited by 108
  15. 2021Deep Learning for Chest X-ray Analysis: A Surveycited by 84
  16. 2020Creation and Validation of a Chest X-Ray Dataset with Eye-tracking and Report Dictation for AI Developmentcited by 56
  17. 2016DeepGaze II: Reading fixations from deep features trained on object recognitioncited by 55
  18. 2018EML-NET:An Expandable Multi-Layer NETwork for Saliency Predictioncited by 51
  19. 2019Contextual Encoder-Decoder Network for Visual Saliency Predictioncited by 44
  20. 2016Shallow and Deep Convolutional Networks for Saliency Predictioncited by 42
  21. 2016Gaze Embeddings for Zero-Shot Image Classificationcited by 25
  22. 2020Tidying Deep Saliency Prediction Architecturescited by 20
  23. 2020Automated Methods for Detection and Classification Pneumonia based on X-Ray Images Using Deep Learningcited by 9
  24. 2018Influence of Image Classification Accuracy on Saliency Map Estimationcited by 2

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 2.

4 papers.

  1. 2022Probabilistic Integration of Object Level Annotations in Chest X-ray Classificationcited by 10
  2. 2023I-AI: A Controllable & Interpretable AI System for Decoding Radiologists' Intense Focus for Accurate CXR Diagnosescited by 10
  3. 2025Weakly Supervised Intracranial Aneurysm Detection and Segmentation in MR angiography via Multi-task UNet with Vesselness Priorcited by 2
  4. 2025Fully Automatic Data Labeling for Ultrasound Screen Detectioncited by 0