Joint chest X-ray diagnosis and clinical visual attention prediction with multi-stage cooperative learning: enhancing interpretability
<|reference_start|>Joint chest X-ray diagnosis and clinical visual attention prediction with multi-stage cooperative learning: enhancing interpretability: As deep learning has become the state-of-the-art for computer-assisted diagnosis, interpretability of the automatic decisions is crucial for clinical deployment. While various methods were proposed in this domain, visual attention maps of clinicians during radiological screening offer a unique asset to provide important insights and can potentially enhance the quality of computer-assisted diagnosis. With this paper, we introduce a novel deep-learning framework for joint disease diagnosis and prediction of corresponding visual saliency maps for chest X-ray scans. Specifically, we designed a novel dual-encoder multi-task UNet, which leverages both a DenseNet201 backbone and a Residual and Squeeze-and-Excitation block-based encoder to extract diverse features for saliency map prediction, and a multi-scale feature-fusion classifier to perform disease classification. To tackle the issue of asynchronous training schedules of individual tasks in multi-task learning, we proposed a multi-stage cooperative learning strategy, with contrastive learning for feature encoder pretraining to boost performance. Experiments show that our proposed method outperformed existing techniques for chest X-ray diagnosis and the quality of visual saliency map prediction.<|reference_end|>
@article{qiu2024joint,
    title={Joint chest X-ray diagnosis and clinical visual attention prediction
  with multi-stage cooperative learning: enhancing interpretability},
    author={Zirui Qiu, Hassan Rivaz, Yiming Xiao},
    journal={arXiv preprint arXiv:2403.16970},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.16970},
    primaryClass={eess.IV cs.CV cs.LG}
}