4 papers
Quantifying Explainable AI-introduced signal noise on ECG data with Spectral Entropy
David A. Kelly, Nathan Blake
Explainability techniques are used to assess the output of various deep learning models. This is especially true in healthcare, where models need to be trusted and decisions justif…
I Am Big, You Are Little; I Am Right, You Are Wrong
David A. Kelly, Akchunya Chanchal, Nathan Blake
Machine learning for image classification is an active and rapidly developing field. With the proliferation of classifiers of different sizes and different architectures, the probl…
3D ReX: Causal Explanations in 3D Neuroimaging Classification
Melane Navaratnarajah, Sophie A. Martin, David A. Kelly +2
Explainability remains a significant problem for AI models in medical imaging, making it challenging for clinicians to trust AI-driven predictions. We introduce 3D ReX, the first c…
SpecReX: Explainable AI for Raman Spectroscopy
Nathan Blake, David A. Kelly, Akchunya Chanchal +3
Raman spectroscopy is becoming more common for medical diagnostics with deep learning models being increasingly used to leverage its full potential. However, the opaque nature of s…