Abstract
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.
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Notes
- 1.
SIIM-ACR Pneumothorax Segmentation: https://www.kaggle.com/competitions/siim-acr-pneumothorax-segmentation/data.
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Qiu, Z., Rivaz, H., Xiao, Y. (2024). Is Visual Explanation with Grad-CAM More Reliable for Deeper Neural Networks? A Case Study with Automatic Pneumothorax Diagnosis. In: Cao, X., Xu, X., Rekik, I., Cui, Z., Ouyang, X. (eds) Machine Learning in Medical Imaging. MLMI 2023. Lecture Notes in Computer Science, vol 14349. Springer, Cham. https://doi.org/10.1007/978-3-031-45676-3_23
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DOI: https://doi.org/10.1007/978-3-031-45676-3_23
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