Novel Semi-Supervised Learning Models to Balance Data Inclusivity and Usability in Healthcare Applications. - Nathan Gaw (2019)

Assessing the validity of saliency maps for abnormality localization in medical imaging. - Nishanth Thumbavanam Arun; Nathan Gaw; Praveer Singh; Ken Chang; Katharina Viktoria Hoebel; Jay B. Patel; Mishka Gidwani; Jayashree Kalpathy-Cramer (CoRR, 2020)

Assessing the (Un)Trustworthiness of Saliency Maps for Localizing Abnormalities in Medical Imaging. - Nishanth Thumbavanam Arun; Nathan Gaw; Praveer Singh; Ken Chang; Mehak Aggarwal; Bryan Chen; Katharina Hoebel; Sharut Gupta; Jay B. Patel; Mishka Gidwani; Julius Adebayo; Matthew D. Li; et al. (CoRR, 2020)

A novel multi-task linear mixed model for smartphone-based telemonitoring. - Hyunsoo Yoon; Nathan Gaw (Expert Syst. Appl., 2021)

A Novel Semi-Supervised Learning Model for Smartphone-Based Health Telemonitoring. - Nathan Gaw; Jing Li 016; Hyunsoo Yoon (IEEE Trans Autom. Sci. Eng., 2024)

Uni-3DAD: Gan-inversion aided universal 3D anomaly detection on model-free products. - Jiayu Liu; Shancong Mou; Nathan Gaw; Yinan Wang (Expert Syst. Appl., 2025)