A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI. - Erico Tjoa; Cuntai Guan (CoRR, 2019)

Enhancing the Extraction of Interpretable Information for Ischemic Stroke Imaging from Deep Neural Networks. - Erico Tjoa; Heng Guo; Yuhao Lu; Cuntai Guan (CoRR, 2019)

Generalization on the Enhancement of Layerwise Relevance Interpretability of Deep Neural Network. - Erico Tjoa; Cuntai Guan (CoRR, 2020)

Quantifying Explainability of Saliency Methods in Deep Neural Networks. - Erico Tjoa; Cuntai Guan (CoRR, 2020)

Convolutional Neural Network Interpretability with General Pattern Theory. - Erico Tjoa; Cuntai Guan (CoRR, 2021)

A Modified Convolutional Network for Auto-encoding based on Pattern Theory Growth Function. - Erico Tjoa (CoRR, 2021)

Two Instances of Interpretable Neural Network for Universal Approximations. - Erico Tjoa; Cuntai Guan (CoRR, 2021)

Self Reward Design with Fine-grained Interpretability. - Erico Tjoa; Cuntai Guan (CoRR, 2021)

A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI. - Erico Tjoa; Cuntai Guan (IEEE Trans. Neural Networks Learn. Syst., 2021)

Improving Deep Neural Network Classification Confidence using Heatmap-based eXplainable AI. - Erico Tjoa; Hong Jing Khok; Tushar Chouhan; Cuntai Guan (CoRR, 2022)

Quantifying Explainability of Saliency Methods in Deep Neural Networks With a Synthetic Dataset. - Erico Tjoa; Cuntai Guan (IEEE Trans. Artif. Intell., 2023)

Enhancing the confidence of deep learning classifiers via interpretable saliency maps. - Erico Tjoa; Hong Jing Khok; Tushar Chouhan; Cuntai Guan (Neurocomputing, 2023)