Abstract
Explainable Deep Learning has gained significant attention in the field of artificial intelligence (AI), particularly in domains such as medical imaging, where accurate and interpretable machine learning models are crucial for effective diagnosis and treatment planning. Grad-CAM is a baseline that highlights the most critical regions of an image used in a deep learning model’s decision-making process, increasing interpretability and trust in the results. It is applied in many computer vision (CV) tasks such as classification and explanation. This study explores the principles of Explainable Deep Learning and its relevance to medical imaging, discusses various explainability techniques and their limitations, and examines medical imaging applications of Grad-CAM. The findings highlight the potential of Explainable Deep Learning and Grad-CAM in improving the accuracy and interpretability of deep learning models in medical imaging. The code is available in (https://github.com/beasthunter758/GradEML).
S. Suara, A. Jha, P. Sinha, and A. A. Sekh—All authors are having equal contributions.
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Funding
The Project funding including hardware resource (GPU) and other costs of the project is funded by the Science and Engineering Research Board (SERB), Govt. of India, Project No: SRG/2022/000122, executed in XIM University, Bhubaneswar, India, supervised by Arif Ahmed Sekh.
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Suara, S., Jha, A., Sinha, P., Sekh, A.A. (2024). Is Grad-CAM Explainable in Medical Images?. In: Kaur, H., Jakhetiya, V., Goyal, P., Khanna, P., Raman, B., Kumar, S. (eds) Computer Vision and Image Processing. CVIP 2023. Communications in Computer and Information Science, vol 2009. Springer, Cham. https://doi.org/10.1007/978-3-031-58181-6_11
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