生物医学工程学杂志最新文献_第3页

[Recurrence prediction of gastric cancer based on multi-resolution feature fusion and context information]. [基于多分辨率特征融合和上下文信息的胃癌复发预测]。

生物医学工程学杂志 Pub Date : 2024-10-25 DOI: 10.7507/1001-5515.202403014

Hongyu Zhou, Haibo Tao, Feiyue Xue, Bin Wang, Huaiping Jin, Zhenhui Li

{"title":"[Recurrence prediction of gastric cancer based on multi-resolution feature fusion and context information].","authors":"Hongyu Zhou, Haibo Tao, Feiyue Xue, Bin Wang, Huaiping Jin, Zhenhui Li","doi":"10.7507/1001-5515.202403014","DOIUrl":"10.7507/1001-5515.202403014","url":null,"abstract":"Pathological images of gastric cancer serve as the gold standard for diagnosing this malignancy. However, the recurrence prediction task often encounters challenges such as insignificant morphological features of the lesions, insufficient fusion of multi-resolution features, and inability to leverage contextual information effectively. To address these issues, a three-stage recurrence prediction method based on pathological images of gastric cancer is proposed. In the first stage, the self-supervised learning framework SimCLR was adopted to train low-resolution patch images, aiming to diminish the interdependence among diverse tissue images and yield decoupled enhanced features. In the second stage, the obtained low-resolution enhanced features were fused with the corresponding high-resolution unenhanced features to achieve feature complementation across multiple resolutions. In the third stage, to address the position encoding difficulty caused by the large difference in the number of patch images, we performed position encoding based on multi-scale local neighborhoods and employed self-attention mechanism to obtain features with contextual information. The resulting contextual features were further combined with the local features extracted by the convolutional neural network. The evaluation results on clinically collected data showed that, compared with the best performance of traditional methods, the proposed network provided the best accuracy and area under curve (AUC), which were improved by 7.63% and 4.51%, respectively. These results have effectively validated the usefulness of this method in predicting gastric cancer recurrence.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"41 5","pages":"886-894"},"PeriodicalIF":0.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11527765/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[Research on in-vivo electron paramagnetic resonance spectrum classification and radiation dose prediction based on machine learning]. [基于机器学习的体内电子顺磁共振波谱分类和辐射剂量预测研究]。

生物医学工程学杂志 Pub Date : 2024-10-25 DOI: 10.7507/1001-5515.202302015

Guangwei Xiong, Bo Chen, Lei Ma, Longpeng Jia, Shunian Chen, Ke Wu, Jing Ning, Bin Zhu, Junwang Guo

{"title":"[Research on in-vivo electron paramagnetic resonance spectrum classification and radiation dose prediction based on machine learning].","authors":"Guangwei Xiong, Bo Chen, Lei Ma, Longpeng Jia, Shunian Chen, Ke Wu, Jing Ning, Bin Zhu, Junwang Guo","doi":"10.7507/1001-5515.202302015","DOIUrl":"10.7507/1001-5515.202302015","url":null,"abstract":"The in-vivo electron paramagnetic resonance (EPR) method can be used for on-site, rapid, and non-invasive detection of radiation dose to casualties after nuclear and radiation emergencies. For in-vivo EPR spectrum analysis, manual labeling of peaks and calculation of signal intensity are often used, which have problems such as large workload and interference by subjective factors. In this study, a method for automatic classification and identification of in-vivo EPR spectra was established using support vector machine (SVM) technology, which can in-batch and automatically identify and screen out invalid spectra due to vibration and dental surface water interference during in-vivo EPR measurements. In this study, a spectrum analysis method based on genetic algorithm optimization neural network (GA-BPNN) was established, which can automatically identify the radiation-induced signals in in-vivo EPR spectra and predict the radiation doses received by the injured. The experimental results showed that the SVM and GA-BPNN spectrum processing methods established in this study could effectively accomplish the automatic spectra classification and radiation dose prediction, and could meet the needs of dose assessment in nuclear emergency. This study explored the application of machine learning methods in EPR spectrum processing, improved the intelligence level of EPR spectrum processing, and would help to enhance the efficiency of mass EPR spectra processing.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"41 5","pages":"995-1002"},"PeriodicalIF":0.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11527747/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[Small-scale cross-layer fusion network for classification of diabetic retinopathy]. [用于糖尿病视网膜病变分类的小型交叉层融合网络]。

生物医学工程学杂志 Pub Date : 2024-10-25 DOI: 10.7507/1001-5515.202403016

Ying Guo, Shaojie Li

{"title":"[Small-scale cross-layer fusion network for classification of diabetic retinopathy].","authors":"Ying Guo, Shaojie Li","doi":"10.7507/1001-5515.202403016","DOIUrl":"10.7507/1001-5515.202403016","url":null,"abstract":"Deep learning-based automatic classification of diabetic retinopathy (DR) helps to enhance the accuracy and efficiency of auxiliary diagnosis. This paper presents an improved residual network model for classifying DR into five different severity levels. First, the convolution in the first layer of the residual network was replaced with three smaller convolutions to reduce the computational load of the network. Second, to address the issue of inaccurate classification due to minimal differences between different severity levels, a mixed attention mechanism was introduced to make the model focus more on the crucial features of the lesions. Finally, to better extract the morphological features of the lesions in DR images, cross-layer fusion convolutions were used instead of the conventional residual structure. To validate the effectiveness of the improved model, it was applied to the Kaggle Blindness Detection competition dataset APTOS2019. The experimental results demonstrated that the proposed model achieved a classification accuracy of 97.75% and a Kappa value of 0.971 7 for the five DR severity levels. Compared to some existing models, this approach shows significant advantages in classification accuracy and performance.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"41 5","pages":"861-868"},"PeriodicalIF":0.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11527746/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[A review on depth perception techniques in organoid images]. [类器官图像深度感知技术综述]。

生物医学工程学杂志 Pub Date : 2024-10-25 DOI: 10.7507/1001-5515.202404036

Yu Sun, Fengliang Huang, Hanwen Zhang, Hao Jiang, Gangyin Luo

{"title":"[A review on depth perception techniques in organoid images].","authors":"Yu Sun, Fengliang Huang, Hanwen Zhang, Hao Jiang, Gangyin Luo","doi":"10.7507/1001-5515.202404036","DOIUrl":"10.7507/1001-5515.202404036","url":null,"abstract":"Organoids are an in vitro model that can simulate the complex structure and function of tissues in vivo. Functions such as classification, screening and trajectory recognition have been realized through organoid image analysis, but there are still problems such as low accuracy in recognition classification and cell tracking. Deep learning algorithm and organoid image fusion analysis are the most advanced organoid image analysis methods. In this paper, the organoid image depth perception technology is investigated and sorted out, the organoid culture mechanism and its application concept in depth perception are introduced, and the key progress of four depth perception algorithms such as organoid image and classification recognition, pattern detection, image segmentation and dynamic tracking are reviewed respectively, and the performance advantages of different depth models are compared and analyzed. In addition, this paper also summarizes the depth perception technology of various organ images from the aspects of depth perception feature learning, model generalization and multiple evaluation parameters, and prospects the development trend of organoids based on deep learning methods in the future, so as to promote the application of depth perception technology in organoid images. It provides an important reference for the academic research and practical application in this field.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"41 5","pages":"1053-1061"},"PeriodicalIF":0.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11527761/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[Comparative analysis of the impact of repetitive transcranial magnetic stimulation and burst transcranial magnetic stimulation at different frequencies on memory function and neuronal excitability of mice]. [不同频率的重复经颅磁刺激和脉冲经颅磁刺激对小鼠记忆功能和神经元兴奋性影响的比较分析]。

生物医学工程学杂志 Pub Date : 2024-10-25 DOI: 10.7507/1001-5515.202312017

Rui Fu, Haijun Zhu, Chong Ding, Guizhi Xu

{"title":"[Comparative analysis of the impact of repetitive transcranial magnetic stimulation and burst transcranial magnetic stimulation at different frequencies on memory function and neuronal excitability of mice].","authors":"Rui Fu, Haijun Zhu, Chong Ding, Guizhi Xu","doi":"10.7507/1001-5515.202312017","DOIUrl":"10.7507/1001-5515.202312017","url":null,"abstract":"Transcranial magnetic stimulation (TMS) as a non-invasive neuroregulatory technique has been applied in the clinical treatment of neurological and psychiatric diseases. However, the stimulation effects and neural regulatory mechanisms of TMS with different frequencies and modes are not yet clear. This article explores the effects of different frequency repetitive transcranial magnetic stimulation (rTMS) and burst transcranial magnetic stimulation (bTMS) on memory function and neuronal excitability in mice from the perspective of neuroelectrophysiology. In this experiment, 42 Kunming mice aged 8 weeks were randomly divided into pseudo stimulation group and stimulation groups. The stimulation group included rTMS stimulation groups with different frequencies (1, 5, 10 Hz), and bTMS stimulation groups with different frequencies (1, 5, 10 Hz). Among them, the stimulation group received continuous stimulation for 14 days. After the stimulation, the mice underwent new object recognition and platform jumping experiment to test their memory ability. Subsequently, brain slice patch clamp experiment was conducted to analyze the excitability of granulosa cells in the dentate gyrus (DG) of mice. The results showed that compared with the pseudo stimulation group, high-frequency (5, 10 Hz) rTMS and bTMS could improve the memory ability and neuronal excitability of mice, while low-frequency (1 Hz) rTMS and bTMS have no significant effect. For the two stimulation modes at the same frequency, their effects on memory function and neuronal excitability of mice have no significant difference. The results of this study suggest that high-frequency TMS can improve memory function in mice by increasing the excitability of hippocampal DG granule neurons. This article provides experimental and theoretical basis for the mechanism research and clinical application of TMS in improving cognitive function.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"41 5","pages":"935-944"},"PeriodicalIF":0.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11527750/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[A lightweight convolutional neural network for myositis classification from muscle ultrasound images]. [用于肌肉超声图像肌炎分类的轻量级卷积神经网络]。

生物医学工程学杂志 Pub Date : 2024-10-25 DOI: 10.7507/1001-5515.202301023

Hao Tan, Xun Lang, Tao Wang, Bingbing He, Zhiyao Li, Yu Lu, Yufeng Zhang

{"title":"[A lightweight convolutional neural network for myositis classification from muscle ultrasound images].","authors":"Hao Tan, Xun Lang, Tao Wang, Bingbing He, Zhiyao Li, Yu Lu, Yufeng Zhang","doi":"10.7507/1001-5515.202301023","DOIUrl":"10.7507/1001-5515.202301023","url":null,"abstract":"Existing classification methods for myositis ultrasound images have problems of poor classification performance or high computational cost. Motivated by this difficulty, a lightweight neural network based on a soft threshold attention mechanism is proposed to cater for a better IIMs classification. The proposed network was constructed by alternately using depthwise separable convolution (DSC) and conventional convolution (CConv). Moreover, a soft threshold attention mechanism was leveraged to enhance the extraction capabilities of key features. Compared with the current dual-branch feature fusion myositis classification network with the highest classification accuracy, the classification accuracy of the network proposed in this paper increased by 5.9%, reaching 96.1%, and its computational complexity was only 0.25% of the existing method. The obtained results support that the proposed method can provide physicians with more accurate classification results at a lower computational cost, thereby greatly assisting them in their clinical diagnosis.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"41 5","pages":"895-902"},"PeriodicalIF":0.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11527749/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[Feature detection of B-ultrasound images of intussusception in children based on improved YOLOv8n]. [基于改进型 YOLOv8n 的儿童肠套叠 B 超图像特征检测]。

生物医学工程学杂志 Pub Date : 2024-10-25 DOI: 10.7507/1001-5515.202401017

Chenyu Liu, Jian Xu, Ke Li, Lu Wang

{"title":"[Feature detection of B-ultrasound images of intussusception in children based on improved YOLOv8n].","authors":"Chenyu Liu, Jian Xu, Ke Li, Lu Wang","doi":"10.7507/1001-5515.202401017","DOIUrl":"10.7507/1001-5515.202401017","url":null,"abstract":"To assist grassroots sonographers in accurately and rapidly detecting intussusception lesions from children's abdominal ultrasound images, this paper proposes an improved YOLOv8n children's intussusception detection algorithm, called EMC-YOLOv8n. Firstly, the EfficientViT network with a cascaded group attention module was used as the backbone network to enhance the speed of target detection. Secondly, the improved C2fMBC module was used to replace the C2f module in the neck network to reduce network complexity, and the coordinate attention (CA) module was introduced after each C2fMBC module to enhance attention to positional information. Finally, experiments were conducted on the self-built dataset of intussusception in children. The results showed that the recall rate, average detection accuracy (mAP@0.5) and precision of the EMC-YOLOv8n algorithm improved by 3.9%, 2.1% and 0.9%, respectively, compared to the baseline algorithm. Despite slightly increased network parameters and computational load, significant improvements in detection accuracy enable efficient completion of detection tasks, demonstrating substantial economic and social value.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"41 5","pages":"903-910"},"PeriodicalIF":0.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11527744/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[An emerging discipline: brain-computer interfaces medicine]. [新兴学科：脑机接口医学]。

生物医学工程学杂志 Pub Date : 2024-08-25 DOI: 10.7507/1001-5515.202310028

Yanxiao Chen, Zhe Zhang, Fan Wang, Peng Ding, Lei Zhao, Yunfa Fu

{"title":"[An emerging discipline: brain-computer interfaces medicine].","authors":"Yanxiao Chen, Zhe Zhang, Fan Wang, Peng Ding, Lei Zhao, Yunfa Fu","doi":"10.7507/1001-5515.202310028","DOIUrl":"10.7507/1001-5515.202310028","url":null,"abstract":"With the development of brain-computer interface (BCI) technology and its translational application in clinical medicine, BCI medicine has emerged, ushering in profound changes to the practice of medicine, while also bringing forth a series of ethical issues related to BCI medicine. BCI medicine is progressively emerging as a new disciplinary focus, yet to date, there has been limited literature discussing it. Therefore, this paper focuses on BCI medicine, firstly providing an overview of the main potential medical applications of BCI technology. It then defines the discipline, outlines its objectives, methodologies, potential efficacy, and associated translational medical research. Additionally, it discusses the ethics associated with BCI medicine, and introduces the standardized operational procedures for BCI medical applications and the methods for evaluating the efficacy of BCI medical applications. Finally, it anticipates the challenges and future directions of BCI medicine. In the future, BCI medicine may become a new academic discipline or major in higher education. In summary, this article is hoped to provide thoughts and references for the development of the discipline of BCI medicine.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"41 4","pages":"641-649"},"PeriodicalIF":0.0,"publicationDate":"2024-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11366471/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142113070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[A deep transfer learning approach for cross-subject recognition of mental tasks based on functional near-infrared spectroscopy]. [基于功能性近红外光谱的跨主体心理任务识别深度迁移学习方法]。

生物医学工程学杂志 Pub Date : 2024-08-25 DOI: 10.7507/1001-5515.202310002

Yao Zhang, Dongyuan Liu, Feng Gao

{"title":"[A deep transfer learning approach for cross-subject recognition of mental tasks based on functional near-infrared spectroscopy].","authors":"Yao Zhang, Dongyuan Liu, Feng Gao","doi":"10.7507/1001-5515.202310002","DOIUrl":"10.7507/1001-5515.202310002","url":null,"abstract":"In the field of brain-computer interfaces (BCIs) based on functional near-infrared spectroscopy (fNIRS), traditional subject-specific decoding methods suffer from the limitations of long calibration time and low cross-subject generalizability, which restricts the promotion and application of BCI systems in daily life and clinic. To address the above dilemma, this study proposes a novel deep transfer learning approach that combines the revised inception-residual network (rIRN) model and the model-based transfer learning (TL) strategy, referred to as TL-rIRN. This study performed cross-subject recognition experiments on mental arithmetic (MA) and mental singing (MS) tasks to validate the effectiveness and superiority of the TL-rIRN approach. The results show that the TL-rIRN significantly shortens the calibration time, reduces the training time of the target model and the consumption of computational resources, and dramatically enhances the cross-subject decoding performance compared to subject-specific decoding methods and other deep transfer learning methods. To sum up, this study provides a basis for the selection of cross-subject, cross-task, and real-time decoding algorithms for fNIRS-BCI systems, which has potential applications in constructing a convenient and universal BCI system.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"41 4","pages":"673-683"},"PeriodicalIF":0.0,"publicationDate":"2024-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11366474/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142113066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[A review of functional electrical stimulation based on brain-computer interface]. [基于脑机接口的功能性电刺激综述]。

生物医学工程学杂志 Pub Date : 2024-08-25 DOI: 10.7507/1001-5515.202311036

Yao Wang, Yuhan Li, Hongyan Cui, Meng Li, Xiaogang Chen

引用次数: 0