Particle Swarm Optimization for Great Enhancement in Semi-supervised Retinal Vessel Segmentation with Generative Adversarial Networks

Huo, Qiang; Tang, Geyu; Zhang, Feng

doi:10.1007/978-3-030-33327-0_14

Cited by 7 publications

(2 citation statements)

References 16 publications

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“…GANs based on semi-supervised learning were also explored to address the problem of lacking annotated data. Huo, et al [148] proposed a semi-supervised framework that combined GAN and self-training scheme, and they adopted particle swarm optimization (PSO) [149] algorithm to choose the hyperparameters in semi-supervised learning since selftraining is sensitive to hyperparameters. They obtained 0.9550/0.8419 of AUC_ROC and AUC_PR on the DRIVE database when using 0.1 labelled and 0.9 unlabeled data.…”

Section: E Generative Adversarial Network (Gan) For Retinal Vessel Segmentationmentioning

confidence: 99%

Retinal Vessel Segmentation Using Deep Learning: A Review

et al. 2021

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This paper presents a comprehensive review of retinal blood vessel segmentation based on deep learning. The geometric characteristics of retinal vessels reflect the health status of patients and help to diagnose some diseases such as diabetes and hypertension. The accurate diagnosis and timing treatment of these diseases can prevent global blindness of patients. Recently, deep learning algorithms have been rapidly applied to retinal vessel segmentation due to their higher efficiency and accuracy, when compared with manual segmentation and other computer-aided diagnosis techniques. In this work, we reviewed recent publications for retinal vessel segmentation based on deep learning. We surveyed these proposed methods especially the network architectures and figured out the trend of models. We summarized obstacles and key aspects for applying deep learning to retinal vessel segmentation and indicated future research directions. This article will help researchers to construct more advanced and robust models.INDEX TERMS Retinal vessel segmentation, fundus images, deep learning, convolutional neural network.

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Section: E Generative Adversarial Network (Gan) For Retinal Vessel Segmentationmentioning

confidence: 99%

Retinal Vessel Segmentation Using Deep Learning: A Review

et al. 2021

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“…Some of the works proposed are based on optimizing hyperparameters of the learning model by metaheuristic algorithms. Huo et al [10] have a semi-supervised automatic segmentation algorithm using particle swarm optimization (PSO). The hyperparameters of the learning model are optimized based on the PSO fitness function.…”

Section: Introductionmentioning

confidence: 99%

Optimized U-Net Segmentation and Hybrid Res-Net for Brain Tumor MRI Images Classification

Rajaragavi¹,

Rajan²

2022

Intelligent Automation &Amp; Soft Computing

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A brain tumor is a portion of uneven cells, need to be detected earlier for treatment. Magnetic Resonance Imaging (MRI) is a routinely utilized procedure to take brain tumor images. Manual segmentation of tumor is a crucial task and laborious. There is a need for an automated system for segmentation and classification for tumor surgery and medical treatments. This work suggests an efficient brain tumor segmentation and classification based on deep learning techniques. Initially, Squirrel search optimized bidirectional ConvLSTM U-net with attention gate proposed for brain tumour segmentation. Then, the Hybrid Deep ResNet and Inception Model used for classification. Squirrel search optimizer mimics the searching behavior of southern flying squirrels and their well-organized way of movement. Here, the squirrel optimizer is utilized to tune the hyperparameters of the U-net model. In addition, bidirectional attention modules of position and channel modules were added in U-Net to extract more characteristic features. Implementation results on BraTS 2018 datasets show that proposed segmentation and classification outperforms in terms of accuracy, dice score, precision rate, recall rate, and Hausdorff Distance.

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Deep learning technology in vascular image segmentation and disease diagnosis

Du,

Zhuang,

Huang

2024

Journal of Intelligent Medicine

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Blood vessel segmentation is a crucial aspect of medical image processing, aiding medical professionals in more accurate disease analysis and diagnosis. Manual blood vessel segmentation methods are time‐consuming and cumbersome, making the development of automatic segmentation methods essential. The rapid advancements in deep learning technology have introduced new tools and methods for vascular image segmentation. In this review, we provide a comprehensive overview of deep learning‐based blood vessel segmentation methods across various fields, including retinal vessel segmentation, cerebrovascular segmentation, and pulmonary vessel segmentation. Several prevalent diseases, such as retinal vascular diseases, cerebrovascular diseases, pulmonary vascular diseases, and tumors, have posed significant health challenges globally. This review also discusses the application of deep learning technology in disease diagnosis within these contexts. Finally, considering the current research landscape, we discuss existing challenges and potential future developments in blood vessel segmentation. We aim to assist researchers in gaining a comprehensive understanding and designing effective blood vessel segmentation models, ultimately offering opportunities for early disease diagnosis and treatment.

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Particle Swarm Optimization for Great Enhancement in Semi-supervised Retinal Vessel Segmentation with Generative Adversarial Networks

Cited by 7 publications

References 16 publications

Retinal Vessel Segmentation Using Deep Learning: A Review

Retinal Vessel Segmentation Using Deep Learning: A Review

Optimized U-Net Segmentation and Hybrid Res-Net for Brain Tumor MRI Images Classification

Deep learning technology in vascular image segmentation and disease diagnosis

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