All Outputs (2)

Agree to Disagree: When Deep Learning Models With Identical Architectures Produce Distinct Explanations (2022)
Presentation / Conference Contribution
Watson, M., Awwad Shiekh Hasan, B., & Al Moubayed, N. (2022, January). Agree to Disagree: When Deep Learning Models With Identical Architectures Produce Distinct Explanations. Presented at Proc. Winter Conference on Applications of Computer Vision, Waikoloa, HI

Deep Learning of neural networks has progressively become more prominent in healthcare with models reaching, or even surpassing, expert accuracy levels. However, these success stories are tainted by concerning reports on the lack of model transparenc... Read More about Agree to Disagree: When Deep Learning Models With Identical Architectures Produce Distinct Explanations.

Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine Learning (2021)
Presentation / Conference Contribution
Watson, M., & Al Moubayed, N. (2021, January). Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine Learning. Presented at The 25th International Conference on Pattern Recognition (ICPR2020), Milan, Italy

Explainable machine learning has become increasingly prevalent, especially in healthcare where explainable models are vital for ethical and trusted automated decision making. Work on the susceptibility of deep learning models to adversarial attacks h... Read More about Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine Learning.