Kaizhu Huang
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Explore the profile of Kaizhu Huang including associated specialties, affiliations and a list of published articles.
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39
Citations
67
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Recent Articles
1.
Duan S, Yong R, Yuan H, Cai T, Huang K, Hoettges K, et al.
Annu Int Conf IEEE Eng Med Biol Soc
. 2025 Mar;
2024:1-5.
PMID: 40039777
This paper presents a smartphone-assisted microfluidic paper-based analytical device (μPAD), which was applied to detect Alzheimer's disease biomarkers, especially in resource-limited regions. This device implements deep learning (DL)-assisted offline smartphone...
2.
Zhao W, Yang G, Zhang R, Jiang C, Yang C, Yan Y, et al.
Neural Netw
. 2024 Oct;
181:106775.
PMID: 39423498
With the explosive 3D data growth, the urgency of utilizing zero-shot learning to facilitate data labeling becomes evident. Recently, methods transferring language or language-image pre-training models like Contrastive Language-Image Pre-training...
3.
Guo J, Huang K, Zhang R, Yi X
IEEE Trans Pattern Anal Mach Intell
. 2024 Sep;
46(12):11345-11360.
PMID: 39264794
While Graph Neural Networks (GNNs) have achieved enormous success in multiple graph analytical tasks, modern variants mostly rely on the strong inductive bias of homophily. However, real-world networks typically exhibit...
4.
Duan S, Cai T, Liu F, Li Y, Yuan H, Yuan W, et al.
Anal Chim Acta
. 2024 May;
1308:342575.
PMID: 38740448
Background: Alzheimer's disease (AD) is a prevalent neurodegenerative disease with no effective treatment. Efficient and rapid detection plays a crucial role in mitigating and managing AD progression. Deep learning-assisted smartphone-based...
5.
Yang G, Huang K, Zhang R, Yang X
Neural Comput
. 2024 Mar;
36(5):936-962.
PMID: 38457762
Zero-shot learning (ZSL) refers to the design of predictive functions on new classes (unseen classes) of data that have never been seen during training. In a more practical scenario, generalized...
6.
Gao P, Yang X, Zhang R, Guo P, Goulermas J, Huang K
IEEE Trans Cybern
. 2024 Feb;
54(9):5381-5393.
PMID: 38416628
While exogenous variables have a major impact on performance improvement in time series analysis, interseries correlation and time dependence among them are rarely considered in the present continuous methods. The...
7.
Qian Z, Zhang S, Huang K, Wang Q, Yi X, Gu B, et al.
Neural Netw
. 2024 Jan;
172:106117.
PMID: 38232423
Whilst adversarial training has been proven to be one most effective defending method against adversarial attacks for deep neural networks, it suffers from over-fitting on training adversarial data and thus...
8.
Ye Z, Yang G, Jin X, Liu Y, Huang K
IEEE Trans Image Process
. 2023 Jul;
32:4185-4198.
PMID: 37467099
Zero-shot learning (ZSL) aims to identify unseen classes with zero samples during training. Broadly speaking, present ZSL methods usually adopt class-level semantic labels and compare them with instance-level semantic predictions...
9.
Sun J, Yao K, An J, Jing L, Huang K, Huang D
Int J Bioprint
. 2023 Jun;
9(4):717.
PMID: 37323491
48With the growing number of biomaterials and printing technologies, bioprinting has brought about tremendous potential to fabricate biomimetic architectures or living tissue constructs. To make bioprinting and bioprinted constructs more...
10.
Mind the Gap: Alleviating Local Imbalance for Unsupervised Cross-Modality Medical Image Segmentation
Su Z, Yao K, Yang X, Wang Q, Yan Y, Sun J, et al.
IEEE J Biomed Health Inform
. 2023 May;
27(7):3396-3407.
PMID: 37134027
Unsupervised cross-modality medical image adaptation aims to alleviate the severe domain gap between different imaging modalities without using the target domain label. A key in this campaign relies upon aligning...