Joint chest X-ray diagnosis and clinical visual attention prediction with multi-stage cooperative learning: enhancing interpretability

arXiv:2403.16970v1 Announce Type: cross
Abstract: 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 …

abstract art arxiv attention become clinical clinicians computer cs.cv cs.lg decisions deep learning deployment diagnosis domain eess.iv interpretability maps prediction ray screening stage state type visual visual attention x-ray