CNN-GCN Aggregation Enabled Boundary Regression for Biomedical Image Segmentation

Meng, Yanda; Meng, Wei; Gao, Dongxu; Zhao, Yitian; Yang, Xiaoyun; Huang, Xiaowei; Zheng, Yalin

doi:10.1007/978-3-030-59719-1_35

Cited by 45 publications

(29 citation statements)

References 23 publications

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“…In this article, CNNs consist of two converting layers with a 5 × 5 kernel. In the first convolution layer, there are twenty feature maps and in the second there are fifty feature maps [ 26 – 34 , 36 – 38 ].…”

Section: Feature Selectionmentioning

confidence: 99%

“…As an inspiration from VGG-19, the development of ResNet model occurred and it is one of the deepest architectures proposed. Size of the convolutional layers in this ResNet model is of 33 filters, and also the layers here all have the same filters as for output feature map and if the feature map is spited to half due to doubling of number of filters, that will be better for maintaining the time complexity of every layer [ 28 , 38 ]. With the stride value of two, it executes the down sample.…”

Section: Feature Selectionmentioning

confidence: 99%

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Automated Detection of Nonmelanoma Skin Cancer Based on Deep Convolutional Neural Network

Arif

Philip

Ajesh

et al. 2022

Journal of Healthcare Engineering

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One of the deadliest diseases is skin cancer, especially melanoma. The high resemblance between different skin lesions such as melanoma and nevus in the skin colour images increases the complexity of identification and diagnosis. An efficient automated early detection system for skin cancer detection is essential in order to save human lives, time, and effort. In this article, an automatic skin lesion classification system using a pretrained deep learning network and transfer learning was proposed. Here, diagnosing melanoma in premature stages, a detection system has been designed which contains the following digital image processing techniques. First, dermoscopy images of skin were taken and this is subjected to a preprocessing step for noise removal and postprocessing step for image enhancement. Then the processed image undergoes image segmentation using k-means and modified k-means clustering. Second, using feature extraction technology, Gray Level Co-occurrence Matrix, and first order statistics, characteristics are extracted. Features are selected on the basis of Harris Hawks optimization (HHO). Finally, various classifiers are used for predicting the stages and efficiency of the proposed work. Measures of well-known quantities, sensitivity, precision, accuracy, and specificity are used in assessing the efficiency of the suggested method, where higher values were obtained. Compared to the current methods, it is found that the classification rate exceeded the output of the current approaches in the performance of the proposed approach.

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Section: Feature Selectionmentioning

confidence: 99%

Section: Feature Selectionmentioning

confidence: 99%

Automated Detection of Nonmelanoma Skin Cancer Based on Deep Convolutional Neural Network

Arif

Philip

Ajesh

et al. 2022

Journal of Healthcare Engineering

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show abstract

“…The attention module works as a filter between the CNN encoder and the GCN decoder to extract more effective semantic and spatial features. Compared to a previous work from the same authors [ 165 ], this model also extracts feature correlations among different layers in the GCN. Meng et al [ 164 ] also demonstrated the effectiveness of the network in the segmentation of the fetal head in ultrasound images.…”

Section: Case Studies Of Gnn For Medical Diagnosis and Analysismentioning

confidence: 99%

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

Ahmedt-Aristizabal

Armin

Denman

et al. 2021

Sensors

136

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With the advances of data-driven machine learning research, a wide variety of prediction problems have been tackled. It has become critical to explore how machine learning and specifically deep learning methods can be exploited to analyse healthcare data. A major limitation of existing methods has been the focus on grid-like data; however, the structure of physiological recordings are often irregular and unordered, which makes it difficult to conceptualise them as a matrix. As such, graph neural networks have attracted significant attention by exploiting implicit information that resides in a biological system, with interacting nodes connected by edges whose weights can be determined by either temporal associations or anatomical junctions. In this survey, we thoroughly review the different types of graph architectures and their applications in healthcare. We provide an overview of these methods in a systematic manner, organized by their domain of application including functional connectivity, anatomical structure, and electrical-based analysis. We also outline the limitations of existing techniques and discuss potential directions for future research.

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“…Experimental results on CVC-Clinic and ETIS-Larib polyp datasets show the state-of-the-art (SOTA) performances. In addition to the related work on polyp segmentation, there are studies on segmentation approaches [44] – [47] .…”

Section: Related Workmentioning

confidence: 99%

Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning

et al. 2021

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Computer-aided detection, localisation, and segmentation methods can help improve colonoscopy procedures. Even though many methods have been built to tackle automatic detection and segmentation of polyps, benchmarking of state-of-the-art methods still remains an open problem. This is due to the increasing number of researched computer-vision methods that can be applied to polyp datasets. Benchmarking of novel methods can provide a direction to the development of automated polyp detection and segmentation tasks. Furthermore, it ensures that the produced results in the community are reproducible and provide a fair comparison of developed methods. In this paper, we benchmark several recent state-of-the-art methods using Kvasir-SEG, an open-access dataset of colonoscopy images, for polyp detection, localisation, and segmentation evaluating both method accuracy and speed. Whilst, most methods in literature have competitive performance over accuracy, we show that YOLOv4 with a Darknet53 backbone and crossstage-partial connections achieved a better trade-off between an average precision of 0.8513 and mean IoU of 0.8025, and the fastest speed of 48 frames per second for the detection and localisation task. Likewise, the proposed ColonSegNet achieved a competitive dice coefficient of 0.8206 and the best average speed of 182.38 frames per second for the segmentation task. Our comprehensive comparison with various state-ofthe-art methods reveal the importance of benchmarking the deep learning methods for automated real-time polyp identification and delineations that can potentially transform current clinical practices and minimise miss-detection rates.

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CNN-GCN Aggregation Enabled Boundary Regression for Biomedical Image Segmentation

Cited by 45 publications

References 23 publications

Automated Detection of Nonmelanoma Skin Cancer Based on Deep Convolutional Neural Network

Automated Detection of Nonmelanoma Skin Cancer Based on Deep Convolutional Neural Network

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning

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