2023/08/29 by Zirui Qiu, Qiu, Zirui, Hassan Rivaz +3 · 1 citation
Computer Science · Psychology · #Artificial intelligence #Artificial neural network #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Convolutional neural network #Deep learning #Digital Imaging for Blood Diseases #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #FOS: Electrical engineering #Flexibility (engineering) #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Machine learning #Popularity #Psychology #Robustness (evolution) #SAFER #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2308.15172
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2023/08/29 · openalex created_date 2023/08/31 · openalex updated_date 2026/07/28
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. With high flexibility, Gradient-weighted Class Activation Mapping (Grad-CAM) has been widely adopted to offer intuitive visual interpretation of various deep learning models' reasoning processes in computer-assisted diagnosis. However, despite the popularity of the technique, there is still a lack of systematic study on Grad-CAM's performance on different deep learning architectures. In this study, we investigate its robustness and effectiveness across different popular deep learning models, with a focus on the impact of the networks' depths and architecture types, by using a case study of automatic pneumothorax diagnosis in X-ray scans. Our results show that deeper neural networks do not necessarily contribute to a strong improvement of pneumothorax diagnosis accuracy, and the effectiveness of GradCAM also varies among different network architectures.