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Automatic skin lesion classification using a novel densely connected convolutional network integrated with an attention module

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Journal of Ambient Intelligence and Humanized computing 2023年第7期14卷 8943-8956页

作者： Shan, Pufang Chen, Jialei Fu, Chong Cao, Lin Tie, Ming Sham, Chiu-Wing School of Computer Science and Engineering Northeastern University Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China School of Information and Communication Engineering Beijing Information Science and Technology University Beijing100101 China Science and Technology on Space Physics Laboratory Beijing100076 China School of Computer Science The University of Auckland Auckland New Zealand

Automated skin lesion classification in dermoscopy images remains challenging due to the existence of artefacts and intrinsic cutaneous features, diversity of lesion morphology, insufficiency of training data, and class imbalance problem. To address these challenges, we propose a new densely connected convolutional network termed AttDenseNet-121, which is obtained by integrating the convolutional block attention module (CBAM) into DenseNet-121. CBAM is a simple yet efficient attention module, which is further improved by adding a novel pooling layer (multiscale-pooling) to effectively enhance its attention capability. The optimized CBAM strengthens the representation power of DenseNet-121 by emphasizing meaningful features and suppressing unnecessary ones, thus significantly enhancing the classification performance. Besides, to handle the imbalanced dataset, we employ an improved focal loss function rather than the traditional cross-entropy loss function to train AttDenseNet-121. The improved focal loss is calculated from a different perspective compared with the original focal loss. It makes the distribution of positive samples and negative samples in each batch the same as that in the original data, successfully mitigating the negative influence of class imbalance on this multi-class classification task. We conduct extensive experiments on the public benchmark dataset (HAM10000) and the results indicate that our proposed method achieves superior performance compared with that of the baselines and state-of-the-art algorithms. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Convolution

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Segmentation of ischemic stroke lesions in multi-spectral MR images using weighting suppressed FCM and three phase level set 1st

Segmentation of ischemic stroke lesions in multi-spectral MR...

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1st International Workshop on Brainlesion, Brainles 2015 Held in Conjunction with International Conference on medical image computing for Computer-Assisted Intervention, MICCAI 2015

作者： Feng, Chaolu Zhao, Dazhe Huang, Min School of Computer Science and Engineering Northeastern University ShenyangLiaoning110819 China Key Laboratory of Medical Image Computing of Ministry of Education Northeastern University ShenyangLiaoning110819 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University ShenyangLiaoning110819 China

ISBN: (纸本)9783319308579

Accurate segmentation of ischemic lesions is still a challenging task. In this paper, we propose a framework to extract ischemic lesions from multi-spectral MR images. In the proposed framework, MR images of each modality are first segmented into brain tissues and ischemic lesions by weighting suppressed fuzzy c-means. Preliminary lesion segmentation results are then fused among all the imaging modalities by majority voting. The fused segmentation results are finally refined by a three phase level set method. The level set formulation is defined on multi-spectral images with the capability of dealing with intensity inhomogeneities. The proposed framework has been applied to the MICCAI 2015 ISLES challenge. According to the ranking rules of the challenge, the proposed framework took the second place and the fourth place in sub-acute lesion segmentation and acute stroke estimation, respectively. © Springer International Publishing Switzerland 2016.

关键词： Numerical methods

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An efficient modified level set method for brain tissue segmentation

An efficient modified level set method for brain tissue segm...

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International Conference on Information and Automation (ICIA)

作者： Di Jia Jin-Zhu Yang Yi-Fei Zhang Key Laboratory of Medical Image Computing of Ministry of Educaion Northeast University ShenYang 110179 China

The paper presents a new efficient method for brain tissue extraction. Firstly, the speed of segmentation is enhanced through improving classical distance matrix. It can accelerate the distance function convergence faster, and the accuracy is not reduced simultaneously. Secondly, the uniqueness of classical result is changed through the improved method. The evolving lines will be stopped at the same level gray, so the primal fluid can be wiped off. White matter and gray matter are extracted more accurate. Finally, a dynamic condition for ending iteration is presented through comparing the interval frames. The improvement changes the flaw of setting evolving times to end iteration, so it can make the veracity and speed much better. The methods are generally applied to image 2D and 3D segmentation, and the results of experiment indicate that the improvements can make the brain tissue extraction more rapid and accurate, and will be very helpful for doctor to make a definite diagnosis.

关键词： Level set image segmentation Capacitance-voltage characteristics Active contours Biomedical imaging medical diagnostic imaging Brain modeling Merging image edge detection Automation

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An XML-based process definition language for medical image understanding

An XML-based process definition language for medical image u...

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International Conference on Computer Application and System Modeling (ICCASM)

作者： Da-Zhe Zhao Wei Li Jin-Zhu Yang Key Laboratory of Medical Image Computing of Ministry of Education University of Northeastern Shenyang Shenyang China

medical image understanding is a computing process of object recognition in medical image using computer vision, applied mathematics, signal analysis and artificial intelligence. This paper provides a medical image understanding process definition language (MPDL) which aims at solving the problem of object recognition in medical image and satisfying the requisition of stable and high precision medical image analysis software engineering. MPDL supports coding, debugging, maintenance and share of medical image understanding algorithm. A software component model of medical image understanding algorithm and a mechanism of mapping and synchronization between component and process node in medical image understanding process are represented. The method of validating a medical image understanding process definition is showed in addition. The rationality and applicability of this language is validated by a platform of medical image understanding algorithm development which uses MPDL.

关键词： medical diagnostic imaging image segmentation Robustness XML Streaming media Variable speed drives

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A uniform solution to common algorithmic problem by tissue p systems with cell division

A uniform solution to common algorithmic problem by tissue p...

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6th International Conference on Bio-Inspired computing: Theories and Applications, BIC-TA 2011

作者： Niu, Yunyun Pan, Linqiang Pérez-Jiménez, Mario J. Key Laboratory on Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 Hubei China Research Group on Natural Computing Department of Computer Science and Artificial Intelligence University of Sevilla Sevilla 41012 Spain

ISBN: (纸本)9780769545141

Common algorithmic problem is an optimization problem, which has the nice property that several other NPcomplete problems can be reduced to it in linear time. A tissue P system with cell division is a computing model which has two basic characters: intercellular communication and the ability of cell division. The ability of cell division allows us to obtain an exponential amount of cells in linear time and to design cellular solutions to computationally hard problems in polynomial time. We here present an effective solution to the common algorithmic decision problem using a family of recognizer tissue P systems with cell division. © 2011 IEEE.

关键词： Polynomial approximation

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Research on SDN Multi Controller Deployment based on K-means++

Research on SDN Multi Controller Deployment based on K-means...

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2020 International Conference on 5G Mobile Communication and Information Science, MCIS-5G 2020

作者： Yue, Li Junyan, Chen Chuxin, Liang Xiaochun, Lei School of Computer and Information Security Guilin University of Electronic Technology Guilin541004 China Guangxi Cloud Computing and Big Data Collaborative Innovation Center Guilin541001 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China

In large-scale SDN network, a single centralized controller can not meet the demand, and multiple controllers are needed to deal with the problem, which leads to the problem of multi control balanced deployment. In this paper, the topology of SDN switches and links is known. The main research contents are as follows: The mathematical model of SDN multi controller deployment is established, and the appropriate network topology is selected and the multi controller deployment problem is solved. In the research, the number of controllers needed in the network and the switches managed by each controller is determined by the algorithm results, and the mapping relationship between controllers and switches is established. By analyzing the deployment results of the same network topology under different algorithms, the influence of different clustering algorithms on the experimental results is obtained. At the same time, the better deployment experiment results are simulated. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Controllers

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UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformer

arXiv

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arXiv 2021年

作者： Wang, Haonan Cao, Peng Wang, Jiaqi Zaiane, Osmar R. Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University China Amii University of Alberta Edmonton Canada

Most recent semantic segmentation methods adopt a U-Net framework with an encoder-decoder architecture. It is still challenging for U-Net with a simple skip connection scheme to model the global multi-scale context: 1) Not each skip connection setting is effective due to the issue of incompatible feature sets of encoder and decoder stage, even some skip connection negatively influence the segmentation performance;2) The original U-Net is worse than the one without any skip connection on some datasets. Based on our findings, we propose a new segmentation framework, named UCTransNet (with a proposed CTrans module in U-Net), from the channel perspective with attention mechanism. Specifically, the CTrans (Channel Transformer) module is an alternate of the U-Net skip connections, which consists of a sub-module to conduct the multi-scale Channel Cross fusion with Transformer (named CCT) and a sub-module Channel-wise Cross-Attention (named CCA) to guide the fused multi-scale channel-wise information to effectively connect to the decoder features for eliminating the ambiguity. Hence, the proposed connection consisting of the CCT and CCA is able to replace the original skip connection to solve the semantic gaps for an accurate automatic medical image segmentation. The experimental results suggest that our UCTransNet produces more precise segmentation performance and achieves consistent improvements over the state-of-the-art for semantic segmentation across different datasets and conventional architectures involving transformer or U-shaped framework. Code: https://***/McGregorWwww/UCTransNet. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Both Style and Distortion Matter: Dual-Path Unsupervised Domain Adaptation for Panoramic Semantic Segmentation

Both Style and Distortion Matter: Dual-Path Unsupervised Dom...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Xu Zheng Jinjing Zhu Yexin Liu Zidong Cao Chong Fu Lin Wang Northeastern University AI Thrust HKUST(GZ) Key Laboratory of Intelligent Computing in Medical Image Ministry of Education NEU China Dept. of CSE HKUST

The ability of scene understanding has sparked active research for panoramic image semantic segmentation. However, the performance is hampered by distortion of the equirectangular projection (ERP) and a lack of pixel-wise annotations. For this reason, some works treat the ERP and pinhole images equally and transfer knowledge from the pinhole to ERP images via unsupervised domain adaptation (UDA). However, they fail to handle the domain gaps caused by: 1) the inherent differences between camera sensors and captured scenes; 2) the distinct image formats (e.g., ERP and pinhole images). In this paper, we propose a novel yet flexible dual-path UDA framework, DPPASS, taking ERP and tangent projection (TP) images as inputs. To reduce the domain gaps, we propose cross-projection and intra-projection training. The cross-projection training includes tangent-wise feature contrastive training and prediction consistency training. That is, the former formulates the features with the same projection locations as positive examples and vice versa, for the models' awareness of distortion, while the latter ensures the consistency of cross-model predictions between the ERP and TP. Moreover, adversarial intra-projection training is proposed to reduce the inherent gap, between the features of the pinhole images and those of the ERP and TP images, respectively. Importantly, the TP path can be freely removed after training, leading to no additional inference cost. Extensive experiments on two benchmarks show that our DPPASS achieves + 1.06% mIoU increment than the state-of-the-art approaches. https://***/cvpr23/DPPASS

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Modeling and hopf bifurcation analysis of benign worms with quarantine strategy 9th

Modeling and hopf bifurcation analysis of benign worms with ...

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9th International Symposium on Cyberspace Safety and Security, CSS 2017

作者： Yao, Yu Fu, Qiang Sheng, Chuan Yang, Wei Key Laboratory of Medical Image Computing of Ministry of Education Northeastern University Shenyang110004 China College of Compute Science and Engineering Northeastern University Shenyang110819 China Software College Northeastern University Shenyang110819 China

ISBN: (纸本)9783319694702

Since the Morris worm occurred in 1988, malwares have threatened the network persistently. With rapid development of the Internet, network security issues become increasingly serious. Recently, the benign worms become a new active countermeasure to deal with the worm threat. In this paper, we propose a compositive-hybrid benign worm propagation model with quarantine strategy. Usually, quarantine strategy will lead to a time delay, and the worm propagation system will be unstable and out of control with the time delay increases. Then the existence condition and the stability of the positive equilibrium are derived. Through our derivation and analysis, the threshold s0 of Hopf bifurcation is obtained. The system will be stable when time delay s\s0. In addition, numerical experiments are performed and the effect of benign worms is displayed by comparing with the model that do not include benign worms. Furthermore, simulation experiments are carried out to verify our conclusions.. © 2017, Springer International Publishing AG.

关键词： Time delay

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Multi-Needle Particle Implantation Computer Assisted Surgery Based on Virtual Reality

Multi-Needle Particle Implantation Computer Assisted Surgery...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Wenjun Tan Jinsong Wang Peifang Huang Guangqiang Yang Qinghua Zhou Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China College of Computer Science and Engineering Northeastern University Shenyang China

Multi-needle radioactive seed implantation is a scientific and effective minimally invasive technique for tumor treatment. Because of the difficulties in planning, low accuracy of puncture operation and non-standard system, it can not be widely used. In this work, a virtual reality based multi-needle particle implantation surgery planning system is developed to help doctors conduct surgical training and planning, and three-dimensional surgical guide model is constructed to assist doctors in positioning in real surgery. The results show that this work can effectively solve the problems of “unclear insertion” and “inaccurate insertion” in clinical multi-needle particle implantation.

关键词： Training Solid modeling Three-dimensional displays Computational modeling Surgery Data visualization Virtual reality

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