Local Model Agnostic XAI Methodologies Applied to Breast Cancer Malignancy Predictions
Abstract
This thesis examines current state-of-the-art Explainable Artificial Intelligence (XAI) methodologies applicable to breast cancer diagnostics, as well as local model-agnostic XAI methodologies more broadly. It is well known that AI is underutilized in healthcare due to the fact that black box AI methods are largely uninterpretable. The potential for AI to positively affect health care outcomes is massive, and AI adoption by medical practitioners and the community at large will translate to more desirable patient outcomes. The development of XAI is crucial to furthering the integration of AI within healthcare, as it will allow medical practitioners and regulatory bodies to become more comfortable and trusting with respect to AI. The scope of this thesis is to examine XAI as it applies to breast cancer diagnostics specifically. However, as we have chosen to discuss local model-agnostic XAI methodologies, the techniques outlined in this thesis will be applicable to all medical domains.