检索结果-内蒙古大学图书馆

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Delong Zhang Mengqun Jin Peng Cao College of Computer Science and Engineering Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Skin conditions affect 1.9 billion people. Because of a shortage of dermatologists, most cases are seen instead by general practitioners with lower diagnostic accuracy. Current skin disease researches adopt the auto-classification system for improving the accuracy rate of skin disease classification. It is therefore an important task to develop Computer Aided Detection (CAD) systems that can aid/enhance dermatologists workflow and improve the classification performances. However, the long-tailed class distribution in the database and the limitation of ability to achieve a spatially invariant features make this problem challenging. We propose a ST-MetaDiagnosis, which utilizes meta-learning and spatial transform learning to facilitate quick adaptation and generalization of deep neural networks trained on the common diseases data for identification of rare diseases with much less annotated data. In particular, in order to predict the target risk where there are limited data samples, we train a meta-learner with spatial transforming from a set of related risk prediction tasks which learns how a good predictor is learned. The meta-learned can be directly used in target risk prediction, and the limited available samples can be used for further fine-tuning the model performance. Experiments on the recent ISIC 2018 skin lesion classification dataset show that our ST-MetaDiagnosis obtains 64.6% (accuracy) and 64.4% (F1-score) on the diagnosis of actinic keratosis, vascular lesion and dermatofibroma, demonstrating that ST-MetaDiagnosis can improve performance for predicting target risk with low resources comparing with the predictor trained on the limited samples available for this risk.

关键词： Skin Diseases Task analysis Lesions Computational modeling Adaptation models Data models

来源：评论

学校读者我要写书评

暂无评论

A multi-instance tumor subtype classification method for small PET datasets using RA-DL attention module guided deep feature extraction with radiomics features

引用

Computers in Biology and Medicine 2024年 174卷 108461-108461页

作者： Diao, Zhaoshuo Jiang, Huiyan Software College Northeastern University Shenyang110819 China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China

Background: Positron emission tomography (PET) is extensively employed for diagnosing and staging various tumors, including liver cancer, lung cancer, and lymphoma. Accurate subtype classification of tumors plays a crucial role in formulating effective treatment plans for patients. Notably, lymphoma comprises subtypes like diffuse large B-cell lymphoma and Hodgkin's lymphoma, while lung cancer encompasses adenocarcinoma, small cell carcinoma, and squamous cell carcinoma. Similarly, liver cancer consists of subtypes such as cholangiocarcinoma and hepatocellular carcinoma. Consequently, the subtype classification of tumors based on PET images holds immense clinical significance. However, in clinical practice, the number of cases available for each subtype is often limited and imbalanced. Therefore, the primary challenge lies in achieving precise subtype classification using a small dataset. Method: This paper presents a novel approach for tumor subtype classification in small datasets using RA-DL (Radiomics-DeepLearning) attention. To address the limited sample size, Support Vector Machines (SVM) is employed as the classifier for tumor subtypes instead of deep learning methods. Emphasizing the importance of texture information in tumor subtype recognition, radiomics features are extracted from the tumor regions during the feature extraction stage. These features are compressed using an autoencoder to reduce redundancy. In addition to radiomics features, deep features are also extracted from the tumors to leverage the feature extraction capabilities of deep learning. In contrast to existing methods, our proposed approach utilizes the RA-DL-Attention mechanism to guide the deep network in extracting complementary deep features that enhance the expressive capacity of the final features while minimizing redundancy. To address the challenges of limited and imbalanced data, our method avoids using classification labels during deep feature extraction and instead incorporates

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

A Pulmonary Vascular Extraction Algorithm from Chest CT/ CTA images

引用

Journal of Healthcare Engineering 2021年第1期2021卷 5763177页

作者： Xu, Shihui Zhang, Ziming Zhou, Qinghua Shao, Wei Tan, Wenjun Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110189 China College of Computer Science and Engineering Northeastern University Shenyang110189 China

Segmentation of pulmonary vessels in CT/CTA images can help physicians better determine the patient’s condition and treatment. However, due to the complexity of CT images, existing methods have limitations in the segmentation of pulmonary vessels. In this paper, a method based on the separation of pulmonary vessels in CT/CTA images is investigated. The method is divided into two steps: in the first step, the lung parenchyma is extracted using the Unet++ algorithm, which can effectively reduce the oversegmentation rate;in the second step, the pulmonary vessels in the lung parenchyma are extracted using nnUnet. According to the obtained lung parenchyma segmentation results, the "AND" operation is performed on the original image and the lung parenchyma segmentation results, and only the blood vessels within the lung parenchyma are segmented, which reduces the interference of external tissues and improves the segmentation accuracy. The experimental data source used CT/CTA images acquired from the partner hospital. After the experiments were performed on a total of 67 sets of images, the accuracy of CT and CTA images reached 85.1% and 87.7%, respectively. The comparison of whether to segment the lung parenchyma and with other conventional methods was also performed, and the experimental results showed that the algorithm in this paper has high accuracy. Copyright © 2021 Shihui Xu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

关键词： Blood vessels

来源：评论

学校读者我要写书评

暂无评论

Automated Segmentation of Lateral Ventricle in MR images Using Multi-scale Feature Fusion Convolutional Neural Network 25th

Automated Segmentation of Lateral Ventricle in MR Images Usi...

引用

25th International Conference on Pattern Recognition Workshops, ICPR 2020

作者： Ye, Fei Wang, Zhiqiang Hu, Kai Zhu, Sheng Gao, Xieping Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan411105 China College of Medical Imaging and Inspection Xiangnan University Chenzhou423000 China

ISBN: (纸本)9783030687793

Studies have shown that the expansion of the lateral ventricle is closely related to many neurodegenerative diseases, so the segmentation of the lateral ventricle plays an important role in the diagnosis of related diseases. However, traditional segmentation methods are subjective, laborious, and time-consuming. Furthermore, due to the uneven magnetic field, irregular, small, and discontinuous shape of every single slice, the segmentation of the lateral ventricle is still a great challenge. In this paper, we propose an efficient and automatic lateral ventricle segmentation method in magnetic resonance (MR) images using a multi-scale feature fusion convolutional neural network (MFF-Net). First, we create a multi-center clinical dataset with a total of 117 patient MR scans. This dataset comes from two different hospitals and the images have different sampling intervals, different ages, and distinct image dimensions. Second, we present a new multi-scale feature fusion module (MSM) to capture different levels of feature information of lateral ventricles through various receptive fields. In particular, MSM can also extract the multi-scale lateral ventricle region feature information to solve the problem of insufficient feature extraction of small object regions with the deepening of network structure. Finally, extensive experiments have been conducted to evaluate the performance of the proposed MFF-Net. In addition, to verify the performance of the proposed method, we compare MFF-Net with seven state-of-the-art segmentation models. Both quantitative results and visual effects show that our MFF-Net outperforms other models and can achieve more accurate segmentation performance. The results also indicate that our model can be applied in clinical practice and is a feasible method for lateral ventricle segmentation. © 2021, Springer Nature Switzerland AG.

关键词： Magnetic resonance

来源：评论

学校读者我要写书评

暂无评论

EMD-based Adaptive Wavelet Threshold for Pulse Wave Denoising

引用

Chinese Journal of Biomedical Engineering(English Edition) 2015年第1期24卷 1-8页

作者： XU Li-shengl SHEN Yan-hua ZHONG Yue KANG Yan Max Q.-H.Meng Sino -Dutch Biomedical and Information Engineering School Northeastern University Key Laboratory of Medical Image Computing (Northeastern University) Ministry of Education Department of Electronic Engineering The Chinese University of Hong Kong

It is inevitable that noises will be introduced during the acquisition of pulse wave signal, which can result in morphology changes of the original pulse wave,and affect the hemodynamic analysis and diagnosis based on pulse wave signals. In order to remove these noises, an adaptive de-noising method based on empirical mode decomposition(EMD) and wavelet threshold is proposed in this paper. Compared with the wavelet threshold method for denoising pulse wave, the proposed approach is more effective, especially at low signal-to-noise ratio.

关键词： pulse wave de-noising EMD adaptive filter wavelet threshold

来源：评论

学校读者我要写书评

暂无评论

An Improved Method of Blood Vessel Enhancement Based on Hessian Matrix 2020

An Improved Method of Blood Vessel Enhancement Based on Hess...

引用

The Fourth International Symposium on image computing and Digital Medicine

作者： Mingxu Huang Chaolu Feng Dazhe Zhao Northeastern University and School of Computer Science and Engineering China Northeastern University and Key Laboratory of Intelligent Computing in Medical Image (MIIC) China

ISBN: (纸本)9781450389686

The analysis of blood vessel images is of great significance to the diagnosis and treatment of vascular diseases. By performing blood vessel enhancement operations on blood vessel images, it can help clinicians perform diagnoses to reduce workload and time. In the blood vessel enhancement algorithm based on the Hessian matrix, to save time when calculating the Hessian matrix, usually use LoG operation for approximate replacement, resulting in the loss of some details. In this paper, we propose a new calculation method that uses successive small size convolution kernels to convolve the filtering results of the previous scale when calculating the Hessian matrix, thus avoiding excessive calculation time and not losing the details of blood vessel enhancement. We evaluate our method on real image data sets, and the results verify that our method has better performance.

关键词： Convolution Hessian matrix Multiscale Vessel enhancement

来源：评论

学校读者我要写书评

暂无评论

Measure Oriented Cost-Sensitive SVM for 3D nodule detection

Measure Oriented Cost-Sensitive SVM for 3D nodule detection

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Peng Cao Dazhe Zhao Osmar Zaiane Key Laboratory of Medical Image Computing of Ministry of Education Northeastern University China

ISBN: (纸本)9781457702150

The class imbalance issue occurs when training a computer-aided detection (CAD) system for nodules. This imbalance causes poor prediction performance for true nodules. Moreover, the misclassification costs are different between two classes and high sensitivity of true nodules is essential in the detection. In order to eliminate or reduce the false positives while keeping high sensitivity, we present an effective wrapper framework incorporating the evaluation measure of imbalanced data into the objective function of cost sensitive SVM. We improve the performance of classification by simultaneously optimizing the best pair of misclassification cost parameter, feature subset and intrinsic parameters. We evaluated the method on a 3D Lung nodule dataset, showing that the proposed method outperforms many other exiting common methods, as well as specific imbalanced data learning methods, which indicates the effectiveness of our method on the imbalanced and unequal misclassification cost data classification.

关键词： Misclassification internal parameter False-positives Cost functions Objective function HIGH SENSITIVITY

来源：评论

学校读者我要写书评

暂无评论

An automatic partitioning method of CTA head-neck image

An automatic partitioning method of CTA head-neck image

引用

World Congress on intelligent Control and Automation (WCICA)

作者： Gong Zhaoxuan Tan Wenjun Yang Jinzhu Xu Meng jia Bian Zijian Zhao Da-zhe Key laboratory of Medical Image Computing University of Northeastern Springfield Nostate China

The segmentation of vessel in CTA is a challenging task mainly due to bone contact from different regions. The proposed method considers a spatial structure features based partition algorithm which can be separated into three sub-volumes. Firstly, a region growing method was brought forward to remove the background interference, and then added up the bone pixel number in the canter region of head image with specific rules to separate from the proximal layer and the middle layer. Finally, the structure feature of bone profile in neck image was used to separate from middle layer and the distal layer. The experiment results illustrate that the proposed method effectively separate the CTA head-neck image into three sub-volumes in terms of real-timeness, effectiveness and robustness.

关键词： Bones Head Arteries Neck image segmentation Biomedical imaging Partitioning algorithms

来源：评论

学校读者我要写书评

暂无评论

Dynamic Prediction of Intraoperative Hypotension Based on Hemodynamic Monitoring Data With a Transformer-Based Deep Learning Model

Dynamic Prediction of Intraoperative Hypotension Based on He...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Kai Yang Mucheng Ren Jun Xu Xian Zeng Jiangsu Key Laboratory of Intelligent Medical Image Computing School of Future Technology Nanjing University of Information Science and Technology Nanjing China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Timely prediction and intervention for Intraoperative Hypotension (IOH), a prevalent complication associated with general anesthesia, is crucial to prevent severe postoperative outcomes. While existing machine learning methods for IOH prediction have shown promise, they face limitations such as reliance on single data sources and disregard for crucial features like the trend in Arterial Blood Pressure (ABP) waveforms. To address these challenges, this paper proposes a novel multichannel deep learning framework that combines Convolutional Neural Networks (CNN) and Transformers for automatic IOH prediction. The model leverages four types of physiological waveforms, including ABP, ElectroCardioGram (ECG), photoplethysmography (PLE), and Carbon Dioxide (CO2), to capture both local and global information, enhancing information representation. Experimental results on retrospective data of 14,140 adult patients undergoing non-cardiac surgery from VitalDB, a public data repository of vital signs taken during surgeries in 10 operating rooms at Seoul National University Hospital (from January 6, 2005 to March 1, 2014), demonstrate that the proposed model consistently outperforms other methods, achieving superior AUROC values of 0.943, 0.928, and 0.923 at 5, 10, and 15 minutes before the event, respectively. These results demonstrate the powerfulness of multi-modal data source as well as the importance of ABP waveform trends. Additionally, the incorporation of multi-task learning and the attention mechanism validates the effectiveness and superiority of our model, highlighting its potential for proactive clinical interventions and improved postoperative patient outcomes.

关键词： Analytical models Soft sensors Surgery Predictive models Transformers Market research Data models Biomedical monitoring Monitoring Hypotension

来源：评论

学校读者我要写书评

暂无评论

引用

2010 IEEE 5th International Conference on Bio-Inspired computing: Theories and Applications, BIC-TA 2010

作者： Zhang, Zhujin Wang, Shuo Zhang, Xingyi Zhang, Zheng Department of Control Science and Engineering Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074 China School of Computer and Information Science University of South Australia Adelaide SA 5095 Australia Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei 230039 China

ISBN: (纸本)9781424464388

Randí et al. proposed a significant graphical representation for DNA sequences, which is very compact and avoids loss of information. In this paper, we build a fast algorithm for this graphical representation with time complexity O(n2), and find another important advantage in the representation: no degeneracy. Moreover, we propose a new method to do similarity analysis of DNA sequences based on the representation. The approach adopts four elements of covariance matrix as a descriptor, and is illustrated on the first exon of beta-globin genes from 11 different species. © 2010 IEEE.

关键词： DNA sequences

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：