[IJHCS] Assistive heatmaps prototype for promoting eXplainable AI (XAI) in the medical workflow for the breast cancer diagnosis. The work was published in the International Journal of Human-Computer Studies.
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Addressing First Machine Learning eXplainability Models #4
Second, a regroup of publications and software on developing new methods to understand nonlinear predictions of State-Of-The-Art (SOTA) for ML models. The site is called heatmapping.org and has lots of content.
I just found out two exciting and potential ideas to our solution. All information and links are listed as follows.
The first one is SHapley Additive exPlanations (SHAP). SHAP is a unified approach to explaining the output of any ML model. They also have interesting issues regarding breast cancer.
Second, a regroup of publications and software on developing new methods to understand nonlinear predictions of State-Of-The-Art (SOTA) for ML models. The site is called heatmapping.org and has lots of content.