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Your search keyword '"Huang, Xiaohua"' showing total 2,251 results
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2,251 results on '"Huang, Xiaohua"'

204. Extracellular Disintegration of Viral Proteins as an Innovative Strategy for Developing Broad-Spectrum Antivirals Against Coronavirus

Author

Sun, Ke, primary, Ding, Zhe, additional, Jia, Xiaoying, additional, Cheng, Haonan, additional, Li, Yingwen, additional, Wu, Yan, additional, Li, Zhuoyu, additional, Huang, Xiaohua, additional, Pu, Fangxu, additional, Li, Entao, additional, Wang, Guiyou, additional, Wang, Wei, additional, Ding, Yun, additional, Wong, Gary, additional, Chiu, Sandra, additional, Lan, Jiaming, additional, and Hu, Aiguo, additional

Published
2023
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209. Odorant-Binding Proteins and Chemosensory Proteins in Spodoptera frugiperda: From Genome-Wide Identification and Developmental Stage-Related Expression Analysis to the Perception of Host Plant Odors, Sex Pheromones, and Insecticides

Author

Jia, Chen, primary, Mohamed, Amr, additional, Cattaneo, Alberto Maria, additional, Huang, Xiaohua, additional, Keyhani, Nemat O., additional, Gu, Maiqun, additional, Zang, Liansheng, additional, and Zhang, Wei, additional

Published
2023
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221. Adaptive Cross Transformer With Contrastive Learning for Surface Defect Detection

Author

Huang, Xiaohua, Li, Yang, Bao, Yongqiang, and Zheng, Wenming

Abstract

The presence of surface irregularities poses a significant threat to the quality of industrial products. Vision-based surface defect detection, known for its objectivity and stability, is extensively studied. Yet, accurately locating and discerning diverse defects proves challenging due to data scarcity and the diversity of defect types. To address these issues, we propose a new adaptive cross transformer with self-supervised contrastive learning, namely, ACViT-SCL, for surface defect detection. In ACViT, the cross transformer, as a model based on the Transformer architecture, is leveraged to address data scarcity issues through metalearning pipeline. Furthermore, the adaptive cross transformer is proposed to enhance the generalization of the cross Transformer across various defect detection tasks. Finally, the self-supervised contrastive learning (CL) is incorporated to enhance feature distinctiveness, fortifying resilience against diverse defects. To demonstrate the superiority and robustness of the proposed method, the performance comparison between ACViT-SCL and state-of-the-art methods is conducted on three surface defect datasets. The results demonstrate that ACViT-SCL outperforms competing methods in terms of accuracy and generalization ability.

Published
2024
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222. SSA-YOLO: An Improved YOLO for Hot-Rolled Strip Steel Surface Defect Detection

Author

Huang, Xiaohua, Zhu, Jiahao, and Huo, Ying

Abstract

In the manufacturing process of hot-rolled steel strips, various mechanical forces, and environmental conditions can cause surface defects, making their detection crucial for ensuring high-quality product production and preventing significant economic losses in the industry. However, existing models within the you only look once (YOLO) family, commonly employed for steel surface defect detection, have exhibited limited effectiveness. In this article, we propose an improved version of YOLO, namely, YOLO enhanced by a convolution squeeze-and-excitation (CSE) module, Conv2d-BatchNorm-SiLU (CBS) with Swin transformer (CST) module, and adaptive spatial feature fusion (ASFF) detection head module, i.e., SSA-YOLO, specifically tailored for end-to-end surface defect detection. Our approach incorporates several key modifications aimed at improving performance. First, we integrate a channel attention mechanism module into the shallow convolutional network module of the backbone. This enhancement focuses on channel information to improve feature extraction related to small defects while reducing redundant information in candidate boxes. In addition, we fuse a Swin transformer (Swin-T) module into the neck to enhance feature representation for detecting diverse and multiscale defects. Finally, the ASFF is introduced in YOLO to increase cross-interaction between high and low levels in the feature pyramid network (FPN). Experimental results demonstrate the superior performance and effectiveness of our SSA-YOLO model compared to other state-of-the-art models. Our approach achieves higher accuracy and sensitivity in detecting surface defects, offering significant advancements in steel strip production quality control. The code is available at https://github.com/MIPIT-Team/SSA-YOLO.

Published
2024
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225. Deep Learning‐Based Acceleration of Compressed Sensing for Noncontrast‐Enhanced Coronary Magnetic Resonance Angiography in Patients With Suspected Coronary Artery Disease.

Author

Wu, Xi, Deng, Liping, Li, Wanjiang, Peng, Pengfei, Yue, Xun, Tang, Lu, Pu, Qian, Ming, Yue, Zhang, Xiaoyong, Huang, Xiaohua, Chen, Yucheng, Huang, Juan, and Sun, Jiayu

Subjects
MAGNETIC resonance angiography, CORONARY artery disease, COMPRESSED sensing, CORONARY artery stenosis, CORONARY angiography
Abstract

Background: The clinical application of coronary MR angiography (MRA) remains limited due to its long acquisition time and often unsatisfactory image quality. A compressed sensing artificial intelligence (CSAI) framework was recently introduced to overcome these limitations, but its feasibility in coronary MRA is unknown. Purpose: To evaluate the diagnostic performance of noncontrast‐enhanced coronary MRA with CSAI in patients with suspected coronary artery disease (CAD). Study Type: Prospective observational study. Population: A total of 64 consecutive patients (mean age ± standard deviation [SD]: 59 ± 10 years, 48.4% females) with suspected CAD. Field Strength/Sequence: A 3.0‐T, balanced steady‐state free precession sequence. Assessment: Three observers evaluated the image quality for 15 coronary segments of the right and left coronary arteries using a 5‐point scoring system (1 = not visible; 5 = excellent). Image scores ≥3 were considered diagnostic. Furthermore, the detection of CAD with ≥50% stenosis was evaluated in comparison to reference standard coronary computed tomography angiography (CTA). Mean acquisition times for CSAI‐based coronary MRA were measured. Statistical Tests: For each patient, vessel and segment, sensitivity, specificity, and diagnostic accuracy of CSAI‐based coronary MRA for detecting CAD with ≥50% stenosis according to coronary CTA were calculated. Intraclass correlation coefficients (ICCs) were used to assess the interobserver agreement. Results: The mean MR acquisition time ± SD was 8.1 ± 2.4 minutes. Twenty‐five (39.1%) patients had CAD with ≥50% stenosis on coronary CTA and 29 (45.3%) patients on MRA. A total of 885 segments on the CTA images and 818/885 (92.4%) coronary MRA segments were diagnostic (image score ≥3). The sensitivity, specificity, and diagnostic accuracy were as follows: per patient (92.0%, 84.6%, and 87.5%), per vessel (82.9%, 93.4%, and 91.1%), and per segment (77.6%, 98.2%, and 96.6%), respectively. The ICCs for image quality and stenosis assessment were 0.76–0.99 and 0.66–1.00, respectively. Data Conclusion: The image quality and diagnostic performance of coronary MRA with CSAI may show good results in comparison to coronary CTA in patients with suspected CAD. Evidence Level: 1. Technical Efficacy: 2. [ABSTRACT FROM AUTHOR]

Published
2023
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230. Sharply and simultaneously increasing pollutant accumulations in cells of organisms induced by rare earth elements in the environment of Nanjing

Author

Yang, Qing, primary, Cheng, Mengzhu, additional, Zhou, Qing, additional, Wang, Lihong, additional, Jiao, Yunlong, additional, Liu, Yongqiang, additional, Zhang, Shuya, additional, Tan, Li, additional, Gu, Zhenhong, additional, Zhu, Hong, additional, Luo, Hongli, additional, Lin, Daozhe, additional, Liu, Nian, additional, Huang, Xiaohua, additional, and Hu, Ligang, additional

Published
2023
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247. Prediction of postoperative reintervention risk for uterine fibroids using clinical-imaging features and T2WI radiomics before high-intensity focused ultrasound ablation

Author

Qin, Shize, Lin, Zhenyang, Liu, Nian, Zheng, Yulin, Jia, Qing, and Huang, Xiaohua

Abstract

To predict the risk of postoperative reintervention for uterine fibroids using clinical-imaging features and T2WI radiomics before high-intensity focused ultrasound (HIFU) ablation. Among patients with uterine fibroids treated with HIFU from 2019 to 2021, 180 were selected per the inclusion and exclusion criteria (42 reintervention and 138 non-reintervention). All patients were randomly assigned to either the training (n = 125) or validation (n = 55) cohorts. Multivariate analysis was used to determine independent clinical-imaging features of reintervention risk. The Relief and LASSO algorithm were used to select optimal radiomics features. Random forest was used to construct the clinical-imaging model based on independent clinical-imaging features, the radiomics model based on optimal radiomics features, and the combined model incorporating the above features. An independent test cohort of 45 patients with uterine fibroids tested these models. The integrated discrimination index (IDI) was used to compare the discrimination performance of these models. Age (p < .001), fibroid volume (p = .001) and fibroid enhancement degree (p = .001) were identified as independent clinical-imaging features. The combined model had AUCs of 0.821 (95% CI: 0.712–0.931) and 0.818 (95% CI: 0.694–0.943) in the validation and independent test cohorts, respectively. The predictive performance of the combined model was 27.8% (independent test cohort, p < .001) and 29.5% (independent test cohort, p = .001) better than the clinical-imaging and radiomics models, respectively. The combined model can effectively predict the risk of postoperative reintervention for uterine fibroids before HIFU ablation. It is expected to help clinicians to develop accurate, personalized treatment and management plans. Future studies will need to be prospectively validated.

Published
2023
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