Search Results for author:
Found 2 papers, 0 papers with code
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.
Is visual explanation with Grad-CAM more reliable for deeper neural networks? a case study with automatic pneumothorax diagnosis
While deep learning techniques have provided the state-of-the-art performance in various clinical tasks, explainability regarding their decision-making process can greatly enhance the credence of these methods for safer and quicker clinical adoption.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
