no code implementations • 22 Aug 2023 • Larry Huynh, Jin Hong, Ajmal Mian, Hajime Suzuki, Yanqiu Wu, Seyit Camtepe
Quantum-inspired Machine Learning (QiML) is a burgeoning field, receiving global attention from researchers for its potential to leverage principles of quantum mechanics within classical computational frameworks.
no code implementations • 18 May 2023 • Qiankun Zuo, Chi-Man Pun, Yudong Zhang, Hongfei Wang, Jin Hong
In this paper, a novel Multi-resolution Spatiotemporal Enhanced Transformer Denoising (MSETD) network with an adversarially functional diffusion model is proposed to map functional magnetic resonance imaging (fMRI) into effective connectivity for mild cognitive impairment (MCI) analysis.
no code implementations • 24 Nov 2021 • Jin Hong, Yu-Dong Zhang, Weitian Chen
Domain adaptation is crucial for transferring the knowledge from the source labeled CT dataset to the target unlabeled MR dataset in abdominal multi-organ segmentation.
1 code implementation • 13 Sep 2021 • Jin Hong, Simon Chun-Ho Yu, Weitian Chen
In this work, we report a novel unsupervised domain adaptation framework for cross-modality liver segmentation via joint adversarial learning and self-learning.
no code implementations • 23 Jan 2019 • Huimin Wen, Jiajing He, Jin Hong, Fangyu Yue, Yaping Dan
Doping silicon with erbium ions was believed to be one of the most promising approaches but suffers from the aggregation of erbium ions that are efficient non-radiative centers, formed during the standard rapid thermal treatment.
Applied Physics