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检索条件"机构=Key Laboratory of Intelligent Computing in Medical Image MIIC"

共 241 条记录，以下是71-80 订阅

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Skin Cancer image Identification Using Deep Convolutional Neural Networks

Skin Cancer Image Identification Using Deep Convolutional Ne...

引用

International Conference on image, Vision and intelligent Systems, ICIVIS 2023

作者： Zhang, Wenbo Sun, Xiaolin Ma, Shiyi Yang, Jinzhu Grzegorzek, Marcin Wang, Yuexi Li, Chen Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China Institute of Medical Informatics University of Luebeck Luebeck Germany Department of Knowledge Engineering University of Economics in Katowice Katowice Poland Department of Rehabilitation Shengjing Hospital of China Medical University Shenyang China

ISBN: (纸本)9789819708543

Skin cancer is a type of cancer and has emerged as one of the most prevalent types over the past decade. Early diagnosis and treatment are a key factor in the treatment of skin cancer. The traditional clinical diagnosis method of skin cancer is dermoscopy. On account of the complexity of the skin image, occasional instances of missed diagnosis and misdiagnosis arise, leading to delayed optimal treatment for patients. Two different networks, Inception-V3 and ResNet-50, are put forward in this paper. The dataset is separated into two categories, and the network performance is evaluated using evaluation indicators. Findings indicate that the ResNet50 network attains a higher accuracy rate of 88.83% compared to the Inception-V3 network's 83.17% accuracy. Additionally, the other three indicators of ResNet-50 are also surpass those of Inception-V3. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Diagnosis

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Robust and Faster Zeroth-Order Minimax Optimization: Complexity and Applications 38

Robust and Faster Zeroth-Order Minimax Optimization: Complex...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： An, Weixin Liu, Yuanyuan Shang, Fanhua Liu, Hongying Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xidian University China College of Intelligence and Computing Tianjin University China Medical School Tianjin University China Peng Cheng Lab Shenzhen China

Many zeroth-order (ZO) optimization algorithms have been developed to solve nonconvex minimax problems in machine learning and computer vision areas. However, existing ZO minimax algorithms have high complexity and rely on some strict restrictive conditions for ZO estimations. To address these issues, we design a new unified ZO gradient descent extragradient ascent (ZO-GDEGA) algorithm, which reduces the overall complexity to O(dϵ-6) to find an ϵ-stationary point of the function ψ for nonconvex-concave (NC-C) problems, where d is the variable dimension. To the best of our knowledge, ZO-GDEGA is the first ZO algorithm with complexity guarantees to solve stochastic NC-C problems. Moreover, ZO-GDEGA requires weaker conditions on the ZO estimations and achieves more robust theoretical results. As a by-product, ZO-GDEGA has advantages on the condition number for the NC-strongly concave case. Experimentally, ZO-GDEGA can generate more effective poisoning attack data with an average accuracy reduction of 5%. The improved AUC performance also verifies the robustness of gradient estimations. © 2024 Neural information processing systems foundation. All rights reserved.

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The Effect of the Number of Sensor Coils on Sensitivity in Electromagnetic Tomography 4

The Effect of the Number of Sensor Coils on Sensitivity in E...

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4th International Symposium on image computing and Digital Medicine, ISICDM 2020

作者： Xu, Siqi Zhao, Qian Li, Liu Li, Nan Tan, Wenjun College of Physical Science and Technology Shenyang Normal University Shenyang China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China

ISBN: (纸本)9781450389686

Electromagnetic tomography (EMT) is a process tomography based on the principle of electromagnetic induction. It has the advantages of non-intrusive, non-contact and low harm. In the process of image reconstruction, the sensitivity matrix reflects the influence of tiny disturbance on the measured value, which is an important index of image quality. Base on the traditional "O"sensor array structure in this article, Comsol Multiphysics finite element simulation software is used to divide the measured field into several tiny elements. A simulated perturbation method is used to investigate the effect of the number of EMT sensors' midline coils on sensitivity. The sensitivity matrix of sensor arrays with 4 coils ,8 coils ,12 coils ,16 coils and 20 coils is compared by using the method of separation variables. The sensitivity matrix of different coils is imaged by Matlab software. Through comparative analysis, it is concluded that the sensitivity band shape of the coil in the same position is same in different models. All of them are distributed along the arc type between the excitation and detection coils, which presents the middle strong and gradually weakens to the outside. With the increase of the number of coils, the sensitivity band is widened, but the sensitivity value of the central part is slightly weakened. According to this characteristic, it lays a foundation for the practical application of EMT technology and image reconstruction. © 2020 Owner/Author.

关键词： image reconstruction

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Joint feature and task aware multi-task feature learning for Alzheimer's disease diagnosis

Joint feature and task aware multi-task feature learning for...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Cao, Peng Liang, Wei Zhang, Kai Tang, Shanshan Yang, Jinzhu Northeastern University College of Computer Science and Engineering Key Laboratory of Intelligent Computing in Medical Image Shenyang China Northeastern University College of Computer Science and Engineering Shenyang China Northeastern University College of Information Science and Engineering Shenyang China

ISBN: (纸本)9781665401265

Alzheimer's disease (AD) is known as one of the major causes of dementia and is characterized by slow progression over several years. There have been efforts to identify the risk of developing AD in its earliest time. Recently, multi-task feature learning (MTFL) methods with sparsity-inducing ell {2,1}-norm have been widely studied to select a discriminative feature subset from MRI features. However, they ignore the complex relationships among imaging markers and among cognitive outcomes. Constructing the relationships with simple Pearson correlation coefficient may degrade model generalizability. To better capture the complicated but more flexible relationship between the cognitive scores and the neuroimaging measures, we propose a two-stage framework to jointly learn the structure within the feature correlation as well as within the task correlation. Moreover, we propose a dual graph regularization to encode the learned correlation structure. It is able to guide the training procedure of MTFL by incorporating both the inherent correlations. Extensive results on benchmark datasets show that for the proposed FTSMTFL model trained with the dual graph regularization, the proposed joint training framework outperforms existing methods and achieves state-of-the-art cognitive prediction performance of AD. © 2021 IEEE.

关键词： Neuroimaging

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A Pulmonary Artery-Vein Separation algorithm based on the relationship between sub-trees 4

A Pulmonary Artery-Vein Separation algorithm based on the re...

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4th International Symposium on image computing and Digital Medicine, ISICDM 2020

作者： Tan, Wenjun Li, Xiaoshuo Liu, Pan Tan, Wenjun College of Medicine and Biological Information Engineering Northeastern University Shenyang110016 China College of Computer Science and Engineering Northeastern University Shenyang110819 China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China

ISBN: (纸本)9781450389686

This article presents a combined algorithm of pulmonary artery-vein (A/V) separation considering both global and local information, including: the transformation of geometric graph, sub-tree separation, and A/V classification. During the process, geometric graphs are firstly built based on the centerlines;A separation of the adhesion points is performed by applying the information stored in the data structure of the nodes and links in the geometric graphs;Sub-trees are then combined according to the local and global information;The combined sub-trees are growing into vascular trees using 2D area growth method;And finally, classification of the arterial and venous vascular trees based on the knowledge of anatomy. An average accuracy of 85% has been achieved of the overall process with a processing time range between 40 s to 50 s, which indicates good performance and stability of this algorithm. © 2020 ACM.

关键词： Separation

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Automated Thalamus Segmentation in MR images Using Convolutional Networks

Automated Thalamus Segmentation in MR Images Using Convoluti...

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IEEE International Conference of Safety Produce Informatization (IICSPI)

作者： Zhaoxuan Gong Chuang Sun Wei Guo Wenjun Tan Wei Zhou Guodong Zhang Computer Science Shenyang Aerospace University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang China Computer Science Shenyang Aerospace University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang China

ISBN: (数字)9781728177380

ISBN: (纸本)9781728177397

Thalamus segmentation in Magnetic Resonance image is a challenging task because of the high inter-patient anatomical variability in both shape and size between patients. In this work, we proposed a fast and robust segmentation of thalamus using convolutional neural networks. First, cropped image which included the thalamus are extracted. Convolutional neural network (CNN) is then trained to segment the whole thalamus region. Intensity constrained model is constructed to obtain the final segmentation. Our method performs better than other state-of-the-art methods. The experiment results show that our approach achieves the highest dice scores than other methods.

关键词： image segmentation Shape Production Thalamus Convolutional neural networks Pancreas Task analysis

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An Unsupervised Domain Adaptation Model Based on Multi-Level Joint Alignment for Multi-Modal Cardiac image Segmentation

An Unsupervised Domain Adaptation Model Based on Multi-Level...

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2023 International Conference on Biomimetic Intelligence and Robotics

作者： Li, Jiapeng Lv, Yimou Xu, Lisheng Qi, Lin College of Medicine and Biological Information Engineering Northeastern University Shenyang110169 China Engineering Research Center of Medical Imaging and Intelligent Analysis Ministry of Education Shenyang110169 China Key Laboratory of Medical Image Computing Ministry of Education Northeastern University Shenyang110169 China

Unsupervised Domain Adaptation has greatly boosted the performance of multi-modal medical segmentation when there are only source domain labels and no labels in the target domain. Many previous work relies on Convolutional Neural Networks for distribution or instance alignment, however, the receptive field of CNN makes it overly concerned with texture and local semantic information of images, while losing the global style information and semantic information and lacking relationships between local and global semantics. In order to address these issues, we propose a two-stage, multi-level framework for unsupervised domain adaptation, which consists of an image translation network and a Transformer-based domain adaptation segmentation network, jointly aligning the data distribution in the source and target domains from the image, feature and output level through adversarial learning. Experimental results indicate that our method can achieve satisfactory results and outperform other state-of-the-art medical image segmentation methods. © 2023 The Authors. Published by Elsevier B.V.

关键词： Textures

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Incorporating dynamic attention gating mechanism and pre-trained embedding for Chinese clinical named entity recognition

Incorporating dynamic attention gating mechanism and pre-tra...

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2023 International Conference on Biomimetic Intelligence and Robotics

作者： Qiu, Zhenming Pan, Anyu Qi, Lin College of Medicine and Biological Information Engineering Northeastern University Shenyang110169 China Engineering Research Center of Medical Imaging and Intelligent Analysis Ministry of Education Shenyang110169 China Key Laboratory of Medical Image Computing Ministry of Education Northeastern University Shenyang110169 China

Clinical Named Entity Recognition (CNER) serves as a cornerstone in clinical research and healthcare informatics, with its primary goal being the discernment and categorization of clinical terminologies present within Electronic medical Records (EMRs). While deep learning has significantly improved CNER’s performance, these models still face challenges in identifying rare or unseen entities. Our study unveils an innovative DAGM-BiLSTM-CRF model that seamlessly integrates data-driven deep learning strategies with human knowledge to surmount this limitation. The distinctive features of this model lie in its integration of a pre-trained character embedding layer, adept at encapsulating nuanced semantic variations at the granularity of individual characters. Furthermore, it incorporates a bidirectional Long Short-Term Memory (BiLSTM) layer, proficient in deriving contextually relevant information by traversing the sequence from both endpoints. The most noteworthy aspect of our model is the inclusion of a Dynamic Attention Gating Mechanism (DAGM) that dynamically recalibrates attention weights based on the traits of the input sequence and a Conditional Random Fields (CRF) layer that considers interdependencies among output labels within a sequence. The evaluation was carried out on the CCKS-2017 CNER benchmark dataset to underscore the superiority of our model over existing cutting-edge methods, thereby validating its proficiency in deciphering the intricacies of Chinese electronic medical records. © 2023 The Authors. Published by Elsevier B.V.

关键词： Embeddings

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Finite Element Analysis for the Effects of the Descending Aorta Tortuosity on Aortic Hemodynamics

Finite Element Analysis for the Effects of the Descending Ao...

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2022 International Symposium on Biomimetic Intelligence and Robotics, ISBIR 2022

作者： Li, Jiapeng Cao, Xuehao Xu, Lisheng Qi, Lin College of Medicine and Biological Information Engineering Northeastern University Shenyang110169 China Engineering Research Center of Medical Imaging and Intelligent Analysis Ministry of Education Shenyang110169 China Key Laboratory of Medical Image Computing Ministry of Education Northeastern University Shenyang110169 China

Large arteries in the human body, such as the aorta, are susceptible to atherosclerosis, and aortic hemodynamic analysis has been widely used to predict cardiovascular disease. In this study, we aim to explore whether the tortuosity of the descending aorta (DA) is associated with the development of atherosclerosis and to investigate how vessel curvature affects blood flow characteristics. To investigate the relationship between tortuosity of DA and atherosclerosis, six models and different degrees of tortuosity were considered and combined for a total of 36 computational models. The models were numerically calculated by finite element analysis based on Fluid-Structure Interaction (FSI), and hemodynamic parameters including blood flow velocity, Wall Shear Stress (WSS), Oscillatory Shear Index (OSI), and Relative Residence Time (RRT), were calculated that are used to identify the locations of thrombosis and atherosclerosis. Our results showed that flow disturbances such as flow recirculation and secondary flow were more severe in the high-tortuosity vessels compared with the less-tortuosity vessels. © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Proceedings of International Symposium on Biomimetic Intelligence and Robotics.

关键词： Fluid structure interaction

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Segmentation of pulmonary vessels based on MSFM method 22

Segmentation of pulmonary vessels based on MSFM method

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22nd IEEE International Conference on E-Health Networking, Application and Services, HEALTHCOM 2020

作者： Wang, Xiaowei Cheng, Liying Huang, Danyang Gao, Xuanshuang Liang, Daili He, Liuye Zhang, Zhimei Li, Nan Tan, Wenjun School of Physical Science and Technology Shenyang Normal University Shenyang110034 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110189 China

ISBN: (纸本)9781728162676

Accurate segmentation of pulmonary blood vessels from CT images is of great significance for lung disease detection and segmentation of other lung structures. Manual segmentation is difficult to accurately segment vascular tissue for various reasons. Therefore, in view of the existing problems and shortcomings of the existing lung vessel segmentation method, a more efficient lung vessel segmentation algorithm is proposed, that is, the multi-template fast marching method (MSFM algorithm). Firstly, it used the hole filling and maximum inter-class variance algorithm for preprocess. In the process, the lung parenchyma is extracted from the chest CT, and then the lung blood vessels are extracted in the lung parenchymal area using the MSFM algorithm. In the extraction process, the threshold and gradient are used to limit the progress of the process and the lung blood vessels are more accurately segmented. Through experimental verification, the accuracy of lung blood vessel segmentation based on MSFM algorithm is improved. © 2021 IEEE.

关键词： Blood vessels

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