{"title":"Identification of cancer driver genes based on hierarchical weak consensus model","authors":"Gaoshi Li, Zhipeng Hu, Xinlong Luo, Jiafei Liu, Jingli Wu, Wei Peng, Xiaoshu Zhu","doi":"10.1007/s13755-024-00279-6","DOIUrl":"https://doi.org/10.1007/s13755-024-00279-6","url":null,"abstract":"","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140078529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-06DOI: 10.1007/s13755-023-00269-0
Chuanzhen Zhu, Honglun Li, Zhiwei Song, Minbo Jiang, Limei Song, Lin Li, Xuan Wang, Qiang Zheng
{"title":"Jointly constrained group sparse connectivity representation improves early diagnosis of Alzheimer’s disease on routinely acquired T1-weighted imaging-based brain network","authors":"Chuanzhen Zhu, Honglun Li, Zhiwei Song, Minbo Jiang, Limei Song, Lin Li, Xuan Wang, Qiang Zheng","doi":"10.1007/s13755-023-00269-0","DOIUrl":"https://doi.org/10.1007/s13755-023-00269-0","url":null,"abstract":"","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140078239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-06DOI: 10.1007/s13755-024-00276-9
Soroor Laffafchi, Ahmad Ebrahimi, Samira Kafan
{"title":"Efficient management of pulmonary embolism diagnosis using a two-step interconnected machine learning model based on electronic health records data","authors":"Soroor Laffafchi, Ahmad Ebrahimi, Samira Kafan","doi":"10.1007/s13755-024-00276-9","DOIUrl":"https://doi.org/10.1007/s13755-024-00276-9","url":null,"abstract":"","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140078894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-23DOI: 10.1007/s13755-024-00274-x
Fangxu Chen, Wei Peng, Wei Dai, Shoulin Wei, Xiaodong Fu, Li Liu, Lijun Liu
{"title":"Supervised graph contrastive learning for cancer subtype identification through multi-omics data integration","authors":"Fangxu Chen, Wei Peng, Wei Dai, Shoulin Wei, Xiaodong Fu, Li Liu, Lijun Liu","doi":"10.1007/s13755-024-00274-x","DOIUrl":"https://doi.org/10.1007/s13755-024-00274-x","url":null,"abstract":"","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139957499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-23DOI: 10.1007/s13755-024-00270-1
Xiaoli Zhang, Yongxionga Wang, Yiheng Tang, Zhe Wang
{"title":"Adaptive filter of frequency bands based coordinate attention network for EEG-based motor imagery classification","authors":"Xiaoli Zhang, Yongxionga Wang, Yiheng Tang, Zhe Wang","doi":"10.1007/s13755-024-00270-1","DOIUrl":"https://doi.org/10.1007/s13755-024-00270-1","url":null,"abstract":"","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139957664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-23DOI: 10.1007/s13755-024-00273-y
Ziang Liu, Ye Yuan, Cui Zhang, Quan Zhu, Xin-feng Xu, Mei Yuan, Wenjun Tan
{"title":"Hierarchical classification of early microscopic lung nodule based on cascade network","authors":"Ziang Liu, Ye Yuan, Cui Zhang, Quan Zhu, Xin-feng Xu, Mei Yuan, Wenjun Tan","doi":"10.1007/s13755-024-00273-y","DOIUrl":"https://doi.org/10.1007/s13755-024-00273-y","url":null,"abstract":"","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139957609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"LCRNet: local cross-channel recalibration network for liver cancer classification based on CT images","authors":"Qiang Fang, Yue Yang, Hao Wang, Hanxi Sun, Jiangming Chen, Zixiang Chen, Tian Pu, Xiaoqing Zhang, Fubao Liu","doi":"10.1007/s13755-023-00263-6","DOIUrl":"https://doi.org/10.1007/s13755-023-00263-6","url":null,"abstract":"","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138584471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As medical treatments continue to advance rapidly, minimally invasive surgery (MIS) has found extensive applications across various clinical procedures. Accurate identification of medical instruments plays a vital role in comprehending surgical situations and facilitating endoscopic image-guided surgical procedures. However, the endoscopic instrument detection poses a great challenge owing to the narrow operating space, with various interfering factors (e.g. smoke, blood, body fluids) and inevitable issues (e.g. mirror reflection, visual obstruction, illumination variation) in the surgery. To promote surgical efficiency and safety in MIS, this paper proposes a cross-layer aggregated attention detection network (CLAD-Net) for accurate and real-time detection of endoscopic instruments in complex surgical scenarios. We propose a cross-layer aggregation attention module to enhance the fusion of features and raise the effectiveness of lateral propagation of feature information. We propose a composite attention mechanism (CAM) to extract contextual information at different scales and model the importance of each channel in the feature map, mitigate the information loss due to feature fusion, and effectively solve the problem of inconsistent target size and low contrast in complex contexts. Moreover, the proposed feature refinement module (RM) enhances the network's ability to extract target edge and detail information by adaptively adjusting the feature weights to fuse different layers of features. The performance of CLAD-Net was evaluated using a public laparoscopic dataset Cholec80 and another set of neuroendoscopic dataset from Sun Yat-sen University Cancer Center. From both datasets and comparisons, CLAD-Net achieves the of 98.9% and 98.6%, respectively, that is better than advanced detection networks. A video for the real-time detection is presented in the following link: https://github.com/A0268/video-demo.
{"title":"CLAD-Net: cross-layer aggregation attention network for real-time endoscopic instrument detection.","authors":"Xiushun Zhao, Jing Guo, Zhaoshui He, Xiaobing Jiang, Haifang Lou, Depei Li","doi":"10.1007/s13755-023-00260-9","DOIUrl":"10.1007/s13755-023-00260-9","url":null,"abstract":"<p><p>As medical treatments continue to advance rapidly, minimally invasive surgery (MIS) has found extensive applications across various clinical procedures. Accurate identification of medical instruments plays a vital role in comprehending surgical situations and facilitating endoscopic image-guided surgical procedures. However, the endoscopic instrument detection poses a great challenge owing to the narrow operating space, with various interfering factors (e.g. smoke, blood, body fluids) and inevitable issues (e.g. mirror reflection, visual obstruction, illumination variation) in the surgery. To promote surgical efficiency and safety in MIS, this paper proposes a cross-layer aggregated attention detection network (CLAD-Net) for accurate and real-time detection of endoscopic instruments in complex surgical scenarios. We propose a cross-layer aggregation attention module to enhance the fusion of features and raise the effectiveness of lateral propagation of feature information. We propose a composite attention mechanism (CAM) to extract contextual information at different scales and model the importance of each channel in the feature map, mitigate the information loss due to feature fusion, and effectively solve the problem of inconsistent target size and low contrast in complex contexts. Moreover, the proposed feature refinement module (RM) enhances the network's ability to extract target edge and detail information by adaptively adjusting the feature weights to fuse different layers of features. The performance of CLAD-Net was evaluated using a public laparoscopic dataset Cholec80 and another set of neuroendoscopic dataset from Sun Yat-sen University Cancer Center. From both datasets and comparisons, CLAD-Net achieves the <math><mrow><mi>A</mi><msub><mi>P</mi><mrow><mn>0.5</mn></mrow></msub></mrow></math> of 98.9% and 98.6%, respectively, that is better than advanced detection networks. A video for the real-time detection is presented in the following link: https://github.com/A0268/video-demo.</p>","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10678866/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138463415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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