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Published in Medical Image Computing and Computer-Assisted Intervention (MICCAI 2023, oral, STAR Award), 2023
Published in AAAI Conference on Artificial Intelligence (AAAI 2024, oral), 2024
We propose an uncertainty‑regularized evidential regression model that fixes the zero‑gradient issue in evidential learning and improves reliability for medical prediction tasks. :contentReference[oaicite:4]{index=4}
Recommended citation: Ye, K.*, Chen, T., Wei, H., & Zhan, L. (2024). Uncertainty Regularized Evidential Regression. AAAI 2024 (oral).
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Published in Neural Networks, 2024, 2024
We develop BPEN, an evidential deep learning model that produces calibrated posterior estimates for brain imaging tasks, improving trustworthiness in neurodegenerative disease assessment.
Recommended citation: Ye, K.* et al. (2024). BPEN: Brain Posterior Evidential Network for Trustworthy Brain Imaging Analysis. Neural Networks, 2024.
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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