1 citations · 1 across the 4 of their papers we have counts for
5 papers
Unsupervised Denoising of Real Clinical Low Dose Liver CT with Perceptual Attention Networks
Zhilin Guan, Wei Zhang
With the development of deep learning, medical image processing has been widely used to assist clinical research. This paper focuses on the denoising problem of low-dose computed t…
A brain-inspired generative model for EEG-based cognitive state identification
Bin Hu, Zhi-Hong Guan
This article proposes a brain-inspired generative (BIG) model that merges an impulsive-attention neural network and a variational autoencoder (VAE) for identifying cognitive states…
RBA-FE: A Robust Brain-Inspired Audio Feature Extractor for Depression Diagnosis
Yu-Xuan Wu, Ziyan Huang, Bin Hu +1
This article proposes a robust brain-inspired audio feature extractor (RBA-FE) model for depression diagnosis, using an improved hierarchical network architecture. Most deep learni…
ISAM-MTL: Cross-subject multi-task learning model with identifiable spikes and associative memory networks
Junyan Li, Bin Hu, Zhi-Hong Guan
Cross-subject variability in EEG degrades performance of current deep learning models, limiting the development of brain-computer interface (BCI). This paper proposes ISAM-MTL, whi…
AM-MTEEG: Multi-task EEG classification based on impulsive associative memory
Junyan Li, Bin Hu, Zhi-Hong Guan
Electroencephalogram-based brain-computer interface (BCI) has potential applications in various fields, but their development is hindered by limited data and significant cross-indi…