LISA : Enhance the explainability of medical images unifying current XAI techniques
Author:
Affiliation:
1. Sri Lanka Institute of Information Technology,dept. Information technology (Data Science),Malabe,Sri Lanka
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9823959/9823960/09824840.pdf?arnumber=9824840
Reference19 articles.
1. COVID-MobileXpert: On-Device COVID-19 Patient Triage and Follow-up using Chest X-rays
2. “Why Should I Trust You?”: Explaining the Predictions of Any Classifier
3. On the Robustness of Interpretability Methods;melis;CoRR,2018
4. Fooling LIME and SHAP
5. Not Just a Black Box: Learning Important Features Through Propagating Activation Differences;shrikumar;CoRR,2016
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1. Explainable artificial intelligence to increase transparency for revolutionizing healthcare ecosystem and the road ahead;Network Modeling Analysis in Health Informatics and Bioinformatics;2023-12-22
2. Confident-CAM: Improving Heat Map Interpretation in Chest X-Ray Image Classification;2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM);2023-12-05
3. Deep learning-based medical image analysis with explainable transfer learning;2023 International Conference on Computer Engineering and Distance Learning (CEDL);2023-06-29
4. Interpretability Vs Explainability: The Black Box of Machine Learning;2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE);2023-02-16
5. Survey of Explainable AI Techniques in Healthcare;Sensors;2023-01-05
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