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Residual Feature Attentional Fusion Network for Lightweight Chest CT Image Super-Resolution

Computers, Materials & Continua 2023年第6期75卷 5159-5176页

作者： Kun Yang Lei Zhao Xianghui Wang Mingyang Zhang linyan xue Shuang Liu Kun Liu College of Quality and Technical Supervision Hebei UniversityBaoding071002China Hebei Technology Innovation Center for Lightweight of New Energy Vehicle Power System Baoding071002China Postdoctoral Research Station of Optical Engineering Hebei UniversityBaoding071000China

The diagnosis of COVID-19 requires chest computed tomography(CT).High-resolution CT images can provide more diagnostic information to help doctors better diagnose the disease,so it is of clinical importance to study super-resolution(SR)algorithms applied to CT images to improve the reso-lution of CT ***,most of the existing SR algorithms are studied based on natural images,which are not suitable for medical images;and most of these algorithms improve the reconstruction quality by increasing the network depth,which is not suitable for machines with limited *** alleviate these issues,we propose a residual feature attentional fusion network for lightweight chest CT image super-resolution(RFAFN).Specifically,we design a contextual feature extraction block(CFEB)that can extract CT image features more efficiently and accurately than ordinary residual *** addition,we propose a feature-weighted cascading strategy(FWCS)based on attentional feature fusion blocks(AFFB)to utilize the high-frequency detail information extracted by CFEB as much as possible via selectively fusing adjacent level feature ***,we suggest a global hierarchical feature fusion strategy(GHFFS),which can utilize the hierarchical features more effectively than dense concatenation by progressively aggregating the feature information at various *** experiments show that our method performs better than most of the state-of-the-art(SOTA)methods on the COVID-19 chest CT *** detail,the peak signal-to-noise ratio(PSNR)is 0.11 dB and 0.47 dB higher on CTtest1 and CTtest2 at×3 SR compared to the suboptimal method,but the number of parameters and multi-adds are reduced by 22K and 0.43G,*** method can better recover chest CT image quality with fewer computational resources and effectively assist in COVID-19.

关键词： Super-resolution COVID-19 chest CT lightweight network contextual feature extraction attentional feature fusion

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Using MsfNet to Predict the ISUP Grade of Renal Clear Cell Carcinoma in Digital Pathology Images

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Computers, Materials & Continua 2024年第1期78卷 393-410页

作者： Kun Yang Shilong Chang Yucheng Wang Minghui Wang Jiahui Yang Shuang Liu Kun Liu linyan xue College of Quality and Technical Supervision Hebei UniversityBaoding071002China National and Local Joint Engineering Research Center of Metrology Instrument and System Hebei UniversityBaoding071002China Hebei Technology Innovation Center for Lightweight of New Energy Vehicle Power System Hebei UniversityBaoding071002China

Clear cell renal cell carcinoma(ccRCC)represents the most frequent form of renal cell carcinoma(RCC),and accurate International Society of Urological Pathology(ISUP)grading is crucial for prognosis and treatment *** study presents a new deep network called Multi-scale Fusion Network(MsfNet),which aims to enhance the automatic ISUP grade of ccRCC with digital histopathology pathology *** MsfNet overcomes the limitations of traditional ResNet50 by multi-scale information fusion and dynamic allocation of channel *** model was trained and tested using 90 Hematoxylin and Eosin(H&E)stained whole slide images(WSIs),which were all cropped into 320×320-pixel patches at 40×*** achieved a micro-averaged area under the curve(AUC)of 0.9807,a macro-averaged AUC of 0.9778 on the test *** Gradient-weighted Class Activation Mapping(Grad-CAM)visually demonstrated MsfNet’s ability to distinguish and highlight abnormal areas more effectively than *** t-Distributed Stochastic Neighbor Embedding(t-SNE)plot indicates our model can efficiently extract critical features from images,reducing the impact of noise and redundant *** results suggest that MsfNet offers an accurate ISUP grade of ccRCC in digital images,emphasizing the potential of AI-assisted histopathological systems in clinical practice.

关键词： Renal cell carcinoma computer-aided diagnosis pathology image deep learning machine learning

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MPFracNet:A Deep Learning Algorithm for Metacarpophalangeal Fracture Detection with Varied Difficulties

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Computers, Materials & Continua 2023年第4期75卷 999-1015页

作者： Geng Qin Ping Luo Kaiyuan Li Yufeng Sun Shiwei Wang Xiaoting Li Shuang Liu linyan xue College of Quality and Technical Supervision Hebei UniversityBaoding071002China Hebei Technology Innovation Center for Lightweight of New Energy Vehicle Power System Baoding071002China National&Local Joint Engineering Research Center of Metrology Instrument and System Hebei UniversityBaoding071002China

Due to small size and high occult,metacarpophalangeal fracturediagnosis displays a low accuracy in terms of fracture detection and locationin X-ray *** efficiently detect metacarpophalangeal fractures on Xrayimages as the second opinion for radiologists,we proposed a novel onestageneural network namedMPFracNet based ***,a deformable bottleneck block(DBB)was integrated into the bottleneckto better adapt to the geometric variation of the ***,an integrated feature fusion module(IFFM)was employed to obtain morein-depth semantic and shallow detail ***,Focal Loss andBalanced L1 Loss were introduced to respectively attenuate the imbalancebetween positive and negative classes and the imbalance between detectionand location *** assessed the proposed model on the test set andachieved an AP of 80.4%for the metacarpophalangeal fracture *** estimate the detection performance for fractures with different difficulties,the proposed model was tested on the subsets of metacarpal,phalangeal andtiny fracture test sets and achieved APs of 82.7%,78.5%and 74.9%,*** proposed framework has state-of-the-art performance for detectingmetacarpophalangeal fractures,which has a strong potential application valuein practical clinical environments.

关键词： Deep learning small object detection metacarpophalangeal fractures computer-aided diagnosis(CAD)

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Multi-Classification of Polyps in Colonoscopy Images Based on an Improved Deep Convolutional Neural Network

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Computers, Materials & Continua 2023年第6期75卷 5837-5852页

作者： Shuang Liu Xiao Liu Shilong Chang Yufeng Sun Kaiyuan Li Ya Hou Shiwei Wang Jie Meng Qingliang Zhao Sibei Wu Kun Yang linyan xue College of Quality and Technical Supervision Hebei UniversityBaoding071002China Hebei Technology Innovation Center for Lightweight of New Energy Vehicle Power System Baoding071002China National&Local Joint Engineering Research Center of Metrology Instrument and System Hebei UniversityBaoding071002China College of Electronic Information Engineering Hebei UniversityBaoding071002China Department of Orthopedics Affiliated Hospital of Hebei UniversityBaoding071002China State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics Center for Molecular Imaging and Translational MedicineDepartment of Laboratory MedicineSchool of Public HealthXiamen UniversityXiamen361102China

Achieving accurate classification of colorectal polyps during colonoscopy can avoid unnecessary endoscopic biopsy or *** study aimed to develop a deep learning model that can automatically classify colorectal polyps histologically on white-light and narrow-band imaging(NBI)colonoscopy images based on World Health Organization(WHO)and Workgroup serrAted polypS and Polyposis(WASP)classification criteria for colorectal ***-light and NBI colonoscopy images of colorectal polyps exhibiting pathological results were firstly collected and classified into four categories:conventional adenoma,hyperplastic polyp,sessile serrated adenoma/polyp(SSAP)and normal,among which conventional adenoma could be further divided into three sub-categories of tubular adenoma,villous adenoma and villioustublar adenoma,subsequently the images were re-classified into six *** this paper,we proposed a novel convolutional neural network termed Polyp-DedNet for the four-and six-category classification tasks of colorectal *** on the existing classification network ResNet50,Polyp-DedNet adopted dilated convolution to retain more high-dimensional spatial information and an Efficient Channel Attention(ECA)module to improve the classification performance *** eliminate gridding artifacts caused by dilated convolutions,traditional convolutional layers were used instead of the max pooling layer,and two convolutional layers with progressively decreasing dilation were added at the end of the *** to the inevitable imbalance of medical image data,a regularization method DropBlock and a Class-Balanced(CB)Loss were performed to prevent network ***,the 5-fold cross-validation was adopted to estimate the performance of Polyp-DedNet for the multi-classification task of colorectal *** accuracies of the proposed Polyp-DedNet for the four-and six-category classifications of colorectal polyps were 89.91%±0.92%and 85.13%±1.10%,respectively.

关键词： Colorectal polyps four-and six-category classifications convolutional neural network dilated residual network

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基于sECANet通道注意力机制的肾透明细胞癌病理图像ISUP分级预测

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电子与信息学报 2022年第1期44卷 138-148页

作者：杨昆常世龙王尉丞高聪刘筱刘爽薛林雁河北大学质量技术监督学院保定071002 计量仪器与系统国家地方联合工程研究中心保定071002 河北省新能源汽车动力系统轻量化技术创新中心保定071002

为了对肾透明细胞癌(ccRCC)进行准确核分级以改善肾癌的治疗和预后,该文提出一种新的通道注意力模块sECANet,通过计算特征图中当前通道与临近通道以及当前通道与远距离通道之间的信息交互来获取更多有用的特征。实验中收集了90例患者的... 详细信息

为了对肾透明细胞癌(ccRCC)进行准确核分级以改善肾癌的治疗和预后,该文提出一种新的通道注意力模块sECANet,通过计算特征图中当前通道与临近通道以及当前通道与远距离通道之间的信息交互来获取更多有用的特征。实验中收集了90例患者的肾组织病理图像,进行裁切和增强后采用五折交叉验证法对改进后的网络在Patch级别进行验证。实验结果表明,该文所提出的模型在Patch级别上鉴别ISUP分级的准确率为78.48±3.17%,精确率为79.95±4.37%,召回率为78.43±2.44%,F1分数为78.51±3.04%。进一步地,对每个病例所有Patch的预测结果采用多数投票法得到Image级别的分类结果,所有病例的准确率为88.89%,精确率为89.88%,召回率为87.65%,F1分数为88.51%。因此,sECANet在Patch级别和Image级别上均优于其他注意力机制和基本网络模型ResNet50。据此,该文所构建的病理图像ccRCC ISUP分级模型有良好的诊断效能,可以为患者的治疗和预后提供一定的参考。

关键词：肾透明细胞癌 ISUP分级病理图像深度学习注意力机制

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双蛋白鹰嘴豆酸奶的研制

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粮食加工 2024年第6期49卷 33-37,53页

作者：周三九薛林燕许昌学院食品与药学院河南省食品安全生物标识快检技术重点实验室河南许昌461000

以鹰嘴豆豆浆、复原乳为主要原料,利用复合乳酸菌作为发酵剂研制双蛋白鹰嘴豆酸奶。根据单因素和正交试验确定了该酸奶的最佳工艺为:鹰嘴豆豆浆与复原乳的比例1∶9,白砂糖添加量8.8%,乳酸菌接种量0.2%,发酵时间6 h,此时的酸奶口感细腻... 详细信息

以鹰嘴豆豆浆、复原乳为主要原料,利用复合乳酸菌作为发酵剂研制双蛋白鹰嘴豆酸奶。根据单因素和正交试验确定了该酸奶的最佳工艺为:鹰嘴豆豆浆与复原乳的比例1∶9,白砂糖添加量8.8%,乳酸菌接种量0.2%,发酵时间6 h,此时的酸奶口感细腻、质地均匀、酸味适中,不仅保留了传统酸奶的营养风味,同时融合了鹰嘴豆豆奶独特的香味。最后对该产品进行理化指标和乳酸菌数的测定,其结果为:蛋白质2.75 g/100 g,脂肪2.60 g/100g,酸度75°T,乳酸菌数5.79×10^(7)CFU/mL,均达到GB 19302—2010《食品安全国家标准风味发酵乳》的要求。

关键词：双蛋白鹰嘴豆酸奶发酵工艺

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组织多普勒对高原地区妊娠期糖尿病胎儿心脏功能评估的应用价值

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中国计划生育学杂志 2020年第9期28卷 1479-1481,1485,I0002页

作者：王绚华薛林燕青海省妇幼保健院西宁810007 青海大学附属医院

目的:探讨组织多普勒对高原地区糖尿病孕妇胎儿心功能的评估价值。方法:来自高海拔地区的50例糖尿病孕妇(糖尿病组)和非糖尿病孕妇80例(对照组),年龄及孕周均匹配,采用组织多普勒测量孕妇胎儿二、三尖瓣环处心肌运动速度(Ea,Aa,Sa峰)及... 详细信息

目的:探讨组织多普勒对高原地区糖尿病孕妇胎儿心功能的评估价值。方法:来自高海拔地区的50例糖尿病孕妇(糖尿病组)和非糖尿病孕妇80例(对照组),年龄及孕周均匹配,采用组织多普勒测量孕妇胎儿二、三尖瓣环处心肌运动速度(Ea,Aa,Sa峰)及Tei指数,分析胎儿左、右心室的心肌舒张及收缩功能。结果:两组孕妇胎儿三尖瓣环的运动速度Ea、Aa、Sa指标均大于相应二尖瓣环(P0.05);糖尿病组胎儿心脏二、三尖瓣环Ea、Aa、Sa值均小于对照组,心肌综合指数高于对照组(P<0.05)。结论:妊娠糖尿病孕妇胎儿左、右心室心肌收缩及舒张功能可能受损,组织多普勒对评估胎儿心功能方面具有较好应用价值。

关键词：高原地区妊娠期糖尿病胎儿组织多普勒心功能瓣环运动速度心肌综合指数

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Targeting cholesterol trafficking to mitigate axonal degeneration in hereditary spastic paraplegia

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Neural Regeneration Research 2025年第5期20卷 1397-1398页

作者： Zhenyu Chen xue-Jun Li Department of Biomedical Sciences University of Illinois College of Medicine RockfordRockfordILUSA Department of Bioengineering University of Illinois at ChicagoChicagoILUSA

Axonal degeneration underlies many debilitating diseases including hereditary spastic paraplegia(HSP),a genetically and clinically diverse group of disorders characterized by spasticity and weakness of the lower *** is one significant cause of chronic neurodisability due to the lack of effective treatments and a wide range of onset ages from early childhood to 70 years.

关键词： degeneration diseases hereditary

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整合驻保高校教育资源服务“低碳保定”建设的对策研究

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保定学院学报 2011年第1期24卷 123-127页

作者：薛林雁刘锦江河北大学质量技术监督学院

近年来,保定低碳经济的发展为保定市高等教育资源整合提供了良好的发展机遇,驻保高校应通过有效整合和联动发展为"低碳保定"提供技术和人才支撑。

关键词：驻保高校整合低碳

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Turbulent Agglomeration of Microparticles in a Cylinder Wake Flow Using LES-DEM:Focusing on the Effect of the Reynolds Number

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Journal of Thermal Science 2025年第1期34卷 34-49页

作者： WANG Shuang MU Lin LI xue XIE Jun DONG Ming School of Energy and Power Engineering Dalian University of Technology School of Optical Information and Energy Engineering Wuhan Institute of Technology School of Energy and Environment Shenyang Aerospace University

Turbulent agglomeration is viewed as a promising technology for enhancing fine particle removal *** better understand particle transport,agglomeration behaviors,and fluid-particle interactions,we numerically explored these phenomena under cylindrical vortex wake influence using a coupled large eddy simulation and discrete element method（LES-DEM） *** validity of the LES approach was verified by comparison with available direct numerical simulation（DNS） *** adopted the Johnson-Kendall-Roberts（JKR） contact model for particle-particle *** particle dispersion and agglomeration characteristics of particles with different diameters（dp=2-20 μm） in the laminar and transition of shear layer（TrSL） flow regimes were *** particles were concentrated at the vortex centers,while larger particles accumulated around the *** agglomeration efficiency exhibited an M-shaped profile spanwise（y-direction）.With increasing Reynolds number,the agglomeration efficiency and turbulence intensity *** particle agglomeration efficiency peaks at a certain Reynolds ***,at higher Reynolds numbers,reducing the residence time of particles in the flow field decreases the agglomeration efficiency.

关键词： discrete element method large eddy simulation microparticle particle agglomeration

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