Outputs (2)

Using Model Explanations to Guide Deep Learning Models Towards Consistent Explanations for EHR Data (2022)
Journal Article
Watson, M., Awwad Shekh Hasan, B., & Al Moubayed, N. (2022). Using Model Explanations to Guide Deep Learning Models Towards Consistent Explanations for EHR Data. Scientific Reports, 12(19899), Article 19899. https://doi.org/10.1038/s41598-022-24356-6

It has been shown that identical Deep Learning (DL) architectures will produce distinct explanations when trained with different hyperparameters that are orthogonal to the task (e.g. random seed, training set order). In domains such as healthcare and... Read More about Using Model Explanations to Guide Deep Learning Models Towards Consistent Explanations for EHR Data.

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.