Version 1
: Received: 9 August 2024 / Approved: 12 August 2024 / Online: 12 August 2024 (12:43:02 CEST)
How to cite:
Bhati, D.; Neha, F.; Amiruzzaman, M. A Survey on Explainable Artificial Intelligence (XAI) Techniques for Visualizing Deep Learning Models in Medical Imaging. Preprints2024, 2024080765. https://doi.org/10.20944/preprints202408.0765.v1
Bhati, D.; Neha, F.; Amiruzzaman, M. A Survey on Explainable Artificial Intelligence (XAI) Techniques for Visualizing Deep Learning Models in Medical Imaging. Preprints 2024, 2024080765. https://doi.org/10.20944/preprints202408.0765.v1
Bhati, D.; Neha, F.; Amiruzzaman, M. A Survey on Explainable Artificial Intelligence (XAI) Techniques for Visualizing Deep Learning Models in Medical Imaging. Preprints2024, 2024080765. https://doi.org/10.20944/preprints202408.0765.v1
APA Style
Bhati, D., Neha, F., & Amiruzzaman, M. (2024). A Survey on Explainable Artificial Intelligence (XAI) Techniques for Visualizing Deep Learning Models in Medical Imaging. Preprints. https://doi.org/10.20944/preprints202408.0765.v1
Chicago/Turabian Style
Bhati, D., FNU Neha and Md Amiruzzaman. 2024 "A Survey on Explainable Artificial Intelligence (XAI) Techniques for Visualizing Deep Learning Models in Medical Imaging" Preprints. https://doi.org/10.20944/preprints202408.0765.v1
Abstract
The combination of medical imaging and deep learning has significantly improved diagnostic and prognostic capabilities in the healthcare domain. Nevertheless, the inherent complexity of deep learning models poses challenges in understanding their decision-making processes. Interpretability and visualization techniques have emerged as crucial tools to unravel the black-box nature of these models, providing insights into their inner workings and enhancing trust in their predictions. This survey paper comprehensively examines various interpretation and visualization techniques applied to deep learning models in medical imaging. The paper reviews methodologies, discusses their applications, and evaluates their effectiveness in enhancing the interpretability, reliability, and clinical relevance of deep learning models in medical image analysis.
Keywords
Medical imaging; Deep learning; Machine learning; Explainable AI; Model interpretability
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
