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6th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2009

作者： Chen, Chen Liu, Huilin Wang, Guoren Yu, Lili Key Laboratory of Medical Image Computing Northeastern University Ministry of Education Liaoning Shenyang 110004 China College of Information Science and Engineering Northeastern University Liaoning Shenyang 110004 China

ISBN: (纸本)9780769537351

Because of the large web scale and the information requirement for special field, focuse2825453011d search has attracted more and more people. For the complexity of natural language, there are ambiguous for a word itself, and which will take some trouble for topic filter. For the two main problems, false positive and false negative, this paper proposes two new methods separately. By machine learning, we construct a guide model with the maximum entropy principle, by which we can filter the noise pages out easily and by KNN method, the false negative problem will be solved easily. The experiment shows that our model or method really outperforms the base-line method. © 2009 IEEE.

关键词： Learning systems

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Cross-Domain Retinopathy Classification with OCT images via Disentangling Representation and Adaptation Networks

Cross-Domain Retinopathy Classification with OCT Images via ...

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Optoelectronics Global Conference (OGC)

作者： Wickramaarachchi Minidu Thiranjaya Yuan Li Lei Tao Yuemei Luo Jiangsu Provincial Key Laboratory of Intelligent Medical Image Computing (iMIC) School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing China Jiangsu Provincial Key Laboratory of Intelligent Medical Image Computing (iMIC) School of Computer Science Nanjing University of Information Science and Technology Nanjing China Jiangsu Provincial Key Laboratory of Intelligent Medical Image Computing (iMIC) School of Future Technology Nanjing University of Information Science and Technology Nanjing China

ISBN: (数字)9798350391466

ISBN: (纸本)9798350391473

Deep learning methods have shown significant potential in retinopathy classification using optical coherence tomography (OCT) images. However, substantial challenges arise due to domain shift issues, which are attributable to heterogeneity in data collection devices, variations in subject populations, and discrepancies in imaging protocols and parameters. To address these, we propose a novel approach leveraging Disentangling Representation and Adaptation Networks (DRANet) to enhance the robustness and generalization of retinopathy classification models across different OCT datasets. Our methodology involves developing DRANet, a specialized network architecture designed to disentangle representations of data and facilitate domain adaptation. DRANet includes components for learning invariant representations across domains through adversarial training, ensuring robustness against domain shifts. DRANet encodes individual representations of content and style from both source and target images. Then, it adapts the domain by incorporating the transferred style factor into the content factor, along with learnable weights specified for each domain. This learning framework allows bidirectional domain. After conducting extensive experiments conducted on three public datasets achieved accuracies of $\mathrm{8 8 \%, ~} \mathrm{9 0 \%}$, and $\mathrm{9 2 \%}$ under three cross-domain scenarios, demonstrating the efficacy of our approach in mitigating domain shift effects and improving retinopathy classification performance.

关键词： Training Deep learning Protocols Retinopathy Optical coherence tomography Imaging Coherence Network architecture Data collection Robustness

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Detecting hot topics in Chinese microblog streams based on frequent patterns mining

Detecting hot topics in Chinese microblog streams based on f...

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2012 International Conference on Web Information Systems and Mining, WISM 2012

作者： Xu, Weili Feng, Shi Wang, Lin Wang, Daling Yu, Ge School of Information Science and Engineering Ministry of Education Northeastern University Shenyang 110819 China Key Laboratory of Medical Image Computing Ministry of Education Northeastern University Shenyang 110819 China

ISBN: (纸本)9783642334689

Microblog plays a more and more important role on the emerging and propagation of the public opinion on the Web. Although topic detection has long been a hot research topic, the characteristics of microblog make it a nontrivial task. In this paper, we propose a novel hot topic detection approach based on keyword extraction and frequent patterns mining. We analyze the characteristics of hot topic microblogs and the topical keywords are extracted according to the increasing rate and frequency in Chinese microblog streams. Different from traditional clustering based detection methods, in this paper we treat the short texts of microblogs as transaction items, and apply Apriori algorithm to generate the hot topics. The experiments in the real dataset verify the efficiency and effectiveness of our proposed methods. © Springer-Verlag Berlin Heidelberg 2012.

关键词： Extraction

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Noninvasive prediction of pulmonary hypertension based on finite element analysis and machine learning

Noninvasive prediction of pulmonary hypertension based on fi...

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2018 IEEE International Conference on Information and Automation, ICIA 2018

作者： Zhao, Shuo Zhang, Haoran Wang, Dongyang Xu, Lisheng Qi, Lin School of Sino-Dutch Biomedical and Information Engineering Northeastern University Shenyang110169 China Key Laboratory of Medical Image Computing Ministry of Education Northeastern University Shenyang110169 China

ISBN: (纸本)9781538680698

Geometry morphological features (GMF) of the ventricles were often used to predict the pulmonary hypertension (PH) in the clinical process. This study analyzed the relationship between right ventricular pressure (RVP) and ventricular morphological changes in simplified statistical shape models which were analyzed with finite element analysis method, and the RVP higher than 40 mmHg was adopted as a criterion to determine the presence of PH. Ten GMF features were utilized and three classifiers (decision tree, SVM and random forest) were performed to predict PH and achieved recognition accuracy of 90.7%, 91.4% and 93.5%, respectively. Through this study, the PH can be identified by morphometric features measured from ventricular images. © 2018 IEEE.

关键词： Finite element method

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Research on neural network temperature compensation for embedded weighing systems 2

Research on neural network temperature compensation for embe...

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2nd International Conference on Electrical, Electronics, and Information Engineering, EEIE 2023

作者： Gan, Hui Lv, Meini Zhan, Liping Yan, Zelin Wuzhou University Wuzhou China Guangxi Colleges and Universities Key Laboratory of Image Processing and Intelligent Information System Wuzhou University Wuzhou China Wuzhou Medical College Wuzhou China

ISBN: (纸本)9781510672840

The accuracy of weighing systems is easily affected by sensors, conversion circuits, temperature, and other factors. Aiming at the problem of significant temperature impact during the operation of the weighing system, which leads to a decrease in weighing accuracy. This article proposes a BP neural network method for temperature compensation of the weighing system to further improve the weighing accuracy. Firstly, a weighing system is built based on STM32 to collect system temperature and weighing data. Then, a BP neural network is built using MATLAB, and the calibration data is imported to train the optimal model of the BP neural network. Finally, the model parameters are imported into an embedded system to achieve real-time temperature compensation for the STM32 weighing system. The experimental results show that the accuracy and stability of the weighing system have been improved, and it has good application value. © 2024 SPIE.

关键词： Embedded systems

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Segmentation of Lung Parenchyma Based on FPN++Mask R-CNN Model 2020

Segmentation of Lung Parenchyma Based on FPN++Mask R-CNN Mod...

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The Fourth International Symposium on image computing and Digital Medicine

作者： Xingfei Xu Gen Li Xiaoshuo Li Pan Liu Wenjun Tan College of Computer Science and Engineering Northeastern University China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education China

ISBN: (纸本)9781450389686

In recent years, the number of lung cancer patients has continued to increase. In the process of detecting lung cancer, accurate segmentation of lung parenchyma plays a key role. In this paper, we proposed a method of lung parenchyma segmentation based on FPN++Mask R-CNN neural network model. The model improved original Mask R-CNN networks and optimized the structure of FPN (Feature Pyramid Networks), which is the feature extraction model of Mask R-CNN, by expanding the scale and level of FPN to fuse and extract more picture feature information from different levels. The experimental results show that compared with original Mask R-CNN models, FPN++Mask R-CNN demonstrates better segmentation results.

关键词： Lung parenchyma segmentation Feature Pyramid Networks Mask R-CNN

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ST-MetaDiagnosis: Meta learning with Spatial Transform for rare skin disease Diagnosis

ST-MetaDiagnosis: Meta learning with Spatial Transform for r...

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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

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

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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)

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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...

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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

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A Pulmonary Vascular Extraction Algorithm from Chest CT/ CTA images

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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

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