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Article ; Online: Breast Tumor Ultrasound Image Segmentation Method Based on Improved Residual U-Net Network.

Zhao, Tianyu / Dai, Hang

Computational intelligence and neuroscience

2022 Volume 2022, Page(s) 3905998

Abstract: In order to achieve efficient and accurate breast tumor recognition and diagnosis, this paper proposes a breast tumor ultrasound image segmentation method based on U-Net framework, combined with residual block and attention mechanism. In this method, the ...

Abstract	In order to achieve efficient and accurate breast tumor recognition and diagnosis, this paper proposes a breast tumor ultrasound image segmentation method based on U-Net framework, combined with residual block and attention mechanism. In this method, the residual block is introduced into U-Net network for improvement to avoid the degradation of model performance caused by the gradient disappearance and reduce the training difficulty of deep network. At the same time, considering the features of spatial and channel attention, a fusion attention mechanism is proposed to be introduced into the image analysis model to improve the ability to obtain the feature information of ultrasound images and realize the accurate recognition and extraction of breast tumors. The experimental results show that the Dice index value of the proposed method can reach 0.921, which shows excellent image segmentation performance.
MeSH term(s)	Breast Neoplasms/diagnostic imaging ; Delayed Emergence from Anesthesia ; Female ; Humans ; Image Processing, Computer-Assisted/methods ; Ultrasonography/methods
Language	English
Publishing date	2022-06-25
Publishing country	United States
Document type	Journal Article
ZDB-ID	2388208-6
ISSN	1687-5273 ; 1687-5273
ISSN (online)	1687-5273
ISSN	1687-5273
DOI	10.1155/2022/3905998
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Article ; Online: Tumor Region Location and Classification Based on Fuzzy Logic and Region Merging Image Segmentation Algorithm.

Zhao, Tianyu / Dai, Hang

publication RETRACTED

Journal of healthcare engineering

2021 Volume 2021, Page(s) 1141619

Abstract: Early diagnosis of tumor plays an important role in the improvement of treatment and survival rate of patients. However, breast tumors are difficult to be diagnosed by invasive examination, so medical imaging has become the most intuitive auxiliary ... ...

Abstract	Early diagnosis of tumor plays an important role in the improvement of treatment and survival rate of patients. However, breast tumors are difficult to be diagnosed by invasive examination, so medical imaging has become the most intuitive auxiliary method for breast tumor diagnosis. Although there is no universal perfect method for image segmentation so far, the consensus on the general law of image segmentation has produced considerable research results and methods. In this context, this paper focuses on the breast tumor image segmentation method based on CNN and proposes an improved DCNN method combined with CRF. This method can obtain the information of multiscale and pixels better. The experimental results show that, compared with DCNN without these methods, the segmentation accuracy is significantly improved.
MeSH term(s)	Algorithms ; Breast Neoplasms/diagnostic imaging ; Female ; Fuzzy Logic ; Humans ; Image Processing, Computer-Assisted ; Magnetic Resonance Imaging
Language	English
Publishing date	2021-10-20
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Retracted Publication
ZDB-ID	2545054-2
ISSN	2040-2309 ; 2040-2295
ISSN (online)	2040-2309
ISSN	2040-2295
DOI	10.1155/2021/1141619
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Article ; Online: An important issue of burnout among pre-hospital emergency medical personnel in Chengdu: a cross-sectional study.

Liu, ZhiJiang / Luo, Li / Dai, Hang / Zhang, Bihua / Ma, Lin / Xiang, Tao

BMC emergency medicine

2024 Volume 24, Issue 1, Page(s) 69

Abstract: Objective: This survey aims to comprehensively understand occupational burnout among pre-hospital emergency medical personnel and explore associated risk factors.: Methods: A cross-sectional online survey using a census method was conducted between ... ...

Abstract	Objective: This survey aims to comprehensively understand occupational burnout among pre-hospital emergency medical personnel and explore associated risk factors. Methods: A cross-sectional online survey using a census method was conducted between 15 July, 2023, and ends on 14 August, 2023, in Chengdu, SiChuan province, China. The questionnaire included general demographic information, the Maslach Burnout Inventory-General Survey (MBI-GS) with 15 items, and the Fatigue Scale-14 (FS-14) with 14 items. Univariate analysis was conducted on all variables, followed by multivariate logistic regression models to examine the associations between occupational burnout and the risk factors. Results: A total of 2,299 participants,99.57% completed the survey effectively The participants were from 166 medical institutions in Chengdu, comprising 1,420 nurses (61.50%) and 889 clinical doctors (38.50%). A total of 33.36% participants experienced burnout, predominantly mild (30.27%), followed by moderate (2.78%) and severe (0.3%). Physicians, higher fatigue scores, age, work experience appeared to be related to burnout. Logistic regression models revealed that individuals aged over 50 were less prone to experience burnout compared to medical staff aged 18-30 (OR: 0.269, 95% CI: 0.115-0.627, p = 0.002). Physicians were more prone to experience burnout compared to nursing staff (OR: 0.690, 95% CI: 0.531-0.898, p = 0.006). Those with 0-5 years of experience were more prone to experience burnout compared to those with 6-10 years or over 15 years of experience (OR: 0.734, 95% CI: 0.547-0.986, p = 0.040; OR: 0.559, 95% CI: 0.339-0.924, p = 0.023). Additionally, for each 1-point increase in the fatigue score, the likelihood of burnout in medical staff increased by 1.367 times (OR: 1.367, 95% CI: 1.323-1.412, p < 0.0001). Conclusion: Pre-hospital emergency medical personnel demonstrate a notable prevalence of mild job burnout. These results provide a groundwork for future focus on the various stages of job burnout within pre-hospital emergency staff, alerting hospital and departmental managers to promptly address the mental well-being of their personnel and intervene as needed.
MeSH term(s)	Humans ; Burnout, Professional/epidemiology ; Burnout, Professional/psychology ; Cross-Sectional Studies ; Male ; Female ; Adult ; China/epidemiology ; Middle Aged ; Surveys and Questionnaires ; Risk Factors ; Young Adult ; Emergency Medical Technicians/psychology ; Fatigue/epidemiology ; Physicians/psychology ; Adolescent ; Logistic Models
Language	English
Publishing date	2024-04-23
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2050431-7
ISSN	1471-227X ; 1471-227X
ISSN (online)	1471-227X
ISSN	1471-227X
DOI	10.1186/s12873-024-00984-1
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Article ; Online: Contribution of the 5G Smart First-Aid Care Platform to Achieving High-Quality Prehospital Care.

Xiang, Tao / Zhang, Pei Yong / Zhuo, Guang Ying / Dai, Hang

Journal of medical Internet research

2023 Volume 25, Page(s) e43374

Abstract: China is gradually becoming an aging society, and the necessity for prehospital first-aid care is increasing. However, there is a long-term information blind spot in traditional prehospital first-aid care. Fifth-generation (5G) network has the advantages ...

Abstract	China is gradually becoming an aging society, and the necessity for prehospital first-aid care is increasing. However, there is a long-term information blind spot in traditional prehospital first-aid care. Fifth-generation (5G) network has the advantages of enhanced broadband, multiple connections, and low latency. Combined with the current prehospital first-aid system, the 5G smart medical prehospital first-aid care model creates a new opportunity for the development of prehospital first-aid care. This paper aimed to describe the 5G smart first-aid care platform and offers practical insights into the construction and application of the 5G smart first-aid care platform in small- and medium-sized cities. We first introduced the working principle of the 5G smart first-aid care platform and then chose patients with prehospital chest pain as an example to describe the whole workflow in detail. The application of the 5G smart emergency-care platform is at the stage of pilot exploration in large- and medium-sized cities. Big data statistical analysis of the completed first-aid care tasks has not been performed yet. The 5G smart first-aid care platform realizes real-time interconnection of information between the ambulance and the hospital, performs remote consultation, shortens the treatment time, and enhances treatment efficiency. Future research should focus on quality control analysis of the 5G smart first-aid care platform.
MeSH term(s)	Humans ; Emergency Medical Services ; Ambulances ; Remote Consultation ; Hospitals ; China
Language	English
Publishing date	2023-05-22
Publishing country	Canada
Document type	Journal Article
ZDB-ID	2028830-X
ISSN	1438-8871 ; 1438-8871
ISSN (online)	1438-8871
ISSN	1438-8871
DOI	10.2196/43374
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Article ; Online: Automatic Schelling Point Detection From Meshes.

Chen, Geng / Dai, Hang / Zhou, Tao / Shen, Jianbing / Shao, Ling

IEEE transactions on visualization and computer graphics

2023 Volume 29, Issue 6, Page(s) 2926–2939

Abstract: Mesh Schelling points explain how humans focus on specific regions of a 3D object. They have a large number of important applications in computer graphics and provide valuable information for perceptual psychology studies. However, detecting mesh ... ...

Abstract	Mesh Schelling points explain how humans focus on specific regions of a 3D object. They have a large number of important applications in computer graphics and provide valuable information for perceptual psychology studies. However, detecting mesh Schelling points is time-consuming and expensive since the existing techniques are mostly based on participant observation studies. To overcome these limitations, we propose to employ powerful deep learning techniques to detect mesh Schelling points in an automatic manner, free from participant observation studies. Specifically, we utilize the mesh convolution and pooling operations to extract informative features from mesh objects, and then predict the 3D heat map of Schelling points in an end-to-end manner. In addition, we propose a Deep Schelling Network (DS-Net) to automatically detect the Schelling points, including a multi-scale fusion component and a novel region-specific loss function to improve our network for a better regression of heat maps. To the best of our knowledge, DS-Net is the first deep neural network for detecting Schelling points from 3D meshes. We evaluate DS-Net on a mesh Schelling point dataset obtained from participant observation studies. The experimental results demonstrate that DS-Net is capable of detecting mesh Schelling points effectively and outperforms various state-of-the-art mesh saliency methods and deep learning models, both qualitatively and quantitatively.
Language	English
Publishing date	2023-05-03
Publishing country	United States
Document type	Journal Article
ISSN	1941-0506
ISSN (online)	1941-0506
DOI	10.1109/TVCG.2022.3144143
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Article ; Online: The Nubeam reference-free approach to analyze metagenomic sequencing reads.

Dai, Hang / Guan, Yongtao

Genome research

2020 Volume 30, Issue 9, Page(s) 1364–1375

Abstract: We present Nubeam ( ...

Abstract	We present Nubeam (
MeSH term(s)	Animals ; Female ; Gastrointestinal Microbiome ; Humans ; Metagenomics/methods ; Mice ; RNA, Ribosomal, 16S ; Sequence Analysis, RNA/methods ; Vagina/microbiology ; Whole Genome Sequencing/methods
Chemical Substances	RNA, Ribosomal, 16S
Language	English
Publishing date	2020-09-03
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	1284872-4
ISSN	1549-5469 ; 1088-9051 ; 1054-9803
ISSN (online)	1549-5469
ISSN	1088-9051 ; 1054-9803
DOI	10.1101/gr.261750.120
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Zs.A 3729: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (2.OG) ab Jg. 2022: Lesesaal (EG)
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Article ; Online: Nubeam-dedup: a fast and RAM-efficient tool to de-duplicate sequencing reads without mapping.

Dai, Hang / Guan, Yongtao

Bioinformatics (Oxford, England)

2020 Volume 36, Issue 10, Page(s) 3254–3256

Abstract: Summary: We present Nubeam-dedup, a fast and RAM-efficient tool to de-duplicate sequencing reads without reference genome. Nubeam-dedup represents nucleotides by matrices, transforms reads into products of matrices, and based on which assigns a unique ... ...

Abstract	Summary: We present Nubeam-dedup, a fast and RAM-efficient tool to de-duplicate sequencing reads without reference genome. Nubeam-dedup represents nucleotides by matrices, transforms reads into products of matrices, and based on which assigns a unique number to a read. Thus, duplicate reads can be efficiently removed by using a collisionless hash function. Compared with other state-of-the-art reference-free tools, Nubeam-dedup uses 50-70% of CPU time and 10-15% of RAM. Availability and implementation: Source code in C++ and manual are available at https://github.com/daihang16/nubeamdedup and https://haplotype.org. Supplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	Algorithms ; Genome ; High-Throughput Nucleotide Sequencing ; Sequence Analysis, DNA ; Software
Language	English
Publishing date	2020-02-21
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btaa112
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Zs.A 2374: Show issues

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Je nach Verfügbarkeit (siehe Angabe bei Bestand)
bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
Jg. 1995 - 2021: Lesesall (2.OG)
ab Jg. 2022: Lesesaal (EG)

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Book ; Online: Laplacian ICP for Progressive Registration of 3D Human Head Meshes

Pears, Nick / Dai, Hang / Smith, Will / Sun, Hao

2023

Abstract: We present a progressive 3D registration framework that is a highly-efficient variant of classical non-rigid Iterative Closest Points (N-ICP). Since it uses the Laplace-Beltrami operator for deformation regularisation, we view the overall process as ... ...

Abstract	We present a progressive 3D registration framework that is a highly-efficient variant of classical non-rigid Iterative Closest Points (N-ICP). Since it uses the Laplace-Beltrami operator for deformation regularisation, we view the overall process as Laplacian ICP (L-ICP). This exploits a `small deformation per iteration' assumption and is progressively coarse-to-fine, employing an increasingly flexible deformation model, an increasing number of correspondence sets, and increasingly sophisticated correspondence estimation. Correspondence matching is only permitted within predefined vertex subsets derived from domain-specific feature extractors. Additionally, we present a new benchmark and a pair of evaluation metrics for 3D non-rigid registration, based on annotation transfer. We use this to evaluate our framework on a publicly-available dataset of 3D human head scans (Headspace). The method is robust and only requires a small fraction of the computation time compared to the most popular classical approach, yet has comparable registration performance. Comment: 7 pages, 6 figures
Keywords	Computer Science - Computer Vision and Pattern Recognition
Publishing date	2023-02-04
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: MSeg3D

Li, Jiale / Dai, Hang / Han, Hao / Ding, Yong

Multi-modal 3D Semantic Segmentation for Autonomous Driving

2023

Abstract: LiDAR and camera are two modalities available for 3D semantic segmentation in autonomous driving. The popular LiDAR-only methods severely suffer from inferior segmentation on small and distant objects due to insufficient laser points, while the robust ... ...

Abstract	LiDAR and camera are two modalities available for 3D semantic segmentation in autonomous driving. The popular LiDAR-only methods severely suffer from inferior segmentation on small and distant objects due to insufficient laser points, while the robust multi-modal solution is under-explored, where we investigate three crucial inherent difficulties: modality heterogeneity, limited sensor field of view intersection, and multi-modal data augmentation. We propose a multi-modal 3D semantic segmentation model (MSeg3D) with joint intra-modal feature extraction and inter-modal feature fusion to mitigate the modality heterogeneity. The multi-modal fusion in MSeg3D consists of geometry-based feature fusion GF-Phase, cross-modal feature completion, and semantic-based feature fusion SF-Phase on all visible points. The multi-modal data augmentation is reinvigorated by applying asymmetric transformations on LiDAR point cloud and multi-camera images individually, which benefits the model training with diversified augmentation transformations. MSeg3D achieves state-of-the-art results on nuScenes, Waymo, and SemanticKITTI datasets. Under the malfunctioning multi-camera input and the multi-frame point clouds input, MSeg3D still shows robustness and improves the LiDAR-only baseline. Our code is publicly available at \url{https://github.com/jialeli1/lidarseg3d}. Comment: Accepted to CVPR 2023 (preprint)
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	004
Publishing date	2023-03-15
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Comment on "Comparison of different versions of the quick sequential organ failure assessment for predicting in-hospital mortality of sepsis patients".

Dai, Hang / Wang, Ping / Zhang, Ji-Mei / Luo, Li

World journal of emergency medicine

2022 Volume 13, Issue 2, Page(s) 160

Language	English
Publishing date	2022-03-02
Publishing country	China
Document type	Journal Article
ZDB-ID	2753264-1
ISSN	1920-8642
ISSN	1920-8642
DOI	10.5847/wjem.j.1920-8642.2022.049
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