Deep learning approaches to biomedical image segmentation

Haque, Intisar Rizwan I; Neubert, Jeremiah

doi:10.1016/j.imu.2020.100297

Cited by 269 publications

(113 citation statements)

References 22 publications

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“…Convolutional neural network is the most commonly used deep learning algorithm (Haque & Neubert, 2020). It is a discriminative deep learning algorithm formed by a stack of multiple convolutional and pooling layers (Deng, 2014).…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

“…The network is faster and easier to train due to its utilization of local connections and sharing of weights (Pouyanfar et al, 2018). A typical CNN receives an image as its input and has neurons arranged in a 3D form connecting to only a portion of the previous layer (Haque & Neubert, 2020). The architecture of CNN is presented in series of stages, where the first stages consist of convolution and pooling layers, and the final stage is composed of a fully connected layer (Lecun, Bengio & Hinton, 2015).…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

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Early survey with bibliometric analysis on machine learning approaches in controlling COVID-19 outbreaks

Chiroma

Ezugwu

Jauro

et al. 2020

PeerJ Computer Science

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Background and Objective The COVID-19 pandemic has caused severe mortality across the globe, with the USA as the current epicenter of the COVID-19 epidemic even though the initial outbreak was in Wuhan, China. Many studies successfully applied machine learning to fight COVID-19 pandemic from a different perspective. To the best of the authors’ knowledge, no comprehensive survey with bibliometric analysis has been conducted yet on the adoption of machine learning to fight COVID-19. Therefore, the main goal of this study is to bridge this gap by carrying out an in-depth survey with bibliometric analysis on the adoption of machine learning-based technologies to fight COVID-19 pandemic from a different perspective, including an extensive systematic literature review and bibliometric analysis. Methods We applied a literature survey methodology to retrieved data from academic databases and subsequently employed a bibliometric technique to analyze the accessed records. Besides, the concise summary, sources of COVID-19 datasets, taxonomy, synthesis and analysis are presented in this study. It was found that the Convolutional Neural Network (CNN) is mainly utilized in developing COVID-19 diagnosis and prognosis tools, mostly from chest X-ray and chest CT scan images. Similarly, in this study, we performed a bibliometric analysis of machine learning-based COVID-19 related publications in the Scopus and Web of Science citation indexes. Finally, we propose a new perspective for solving the challenges identified as direction for future research. We believe the survey with bibliometric analysis can help researchers easily detect areas that require further development and identify potential collaborators. Results The findings of the analysis presented in this article reveal that machine learning-based COVID-19 diagnose tools received the most considerable attention from researchers. Specifically, the analyses of results show that energy and resources are more dispenses towards COVID-19 automated diagnose tools while COVID-19 drugs and vaccine development remains grossly underexploited. Besides, the machine learning-based algorithm that is predominantly utilized by researchers in developing the diagnostic tool is CNN mainly from X-rays and CT scan images. Conclusions The challenges hindering practical work on the application of machine learning-based technologies to fight COVID-19 and new perspective to solve the identified problems are presented in this article. Furthermore, we believed that the presented survey with bibliometric analysis could make it easier for researchers to identify areas that need further development and possibly identify potential collaborators at author, country and institutional level, with the overall aim of furthering research in the focused area of machine learning application to disease control.

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Section: Convolutional Neural Networkmentioning

confidence: 99%

Section: Convolutional Neural Networkmentioning

confidence: 99%

Early survey with bibliometric analysis on machine learning approaches in controlling COVID-19 outbreaks

Chiroma

Ezugwu

Jauro

et al. 2020

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…Image segmentation that classifies every pixel in an image suffers from pixel level imbalances, as are other computer vision tasks.Some of the well-known image segmentation algorithms include Fully connected network [50], SegNet [51], U-Net [52], ResUNet [53] etc. Image segmentation is essential for a variety of tasks, including: Urban scene segmentation for autonomous driving Imagesegmentationisessentialfor a varietyoftasks, including: Urban scenesegmentationforautonomousdriving [54], Industrial inspection [55] and cancer cell segmentation [56]. Datasets of all these tasks suffer from pixel level imbalance.…”

Section: • Instance Hardness Threshold Based Under-sampling: Instancementioning

confidence: 99%

A Survey on Generative Adversarial Networks for imbalance problems in computer vision tasks

Sampath

Maurtua

Martín

et al. 2020

Preprint

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Any computer vision application development starts off by acquiring images and data, then preprocessing and pattern recognition steps to perform a task. When the acquired images are highly imbalanced and not adequate, the desired task may not be achievable. Unfortunately, the occurrence of imbalance problems in acquired image datasets in certain complex real-world problems such as anomaly detection, emotion recognition, medical image analysis, fraud detection, metallic surface defect detection, disaster prediction, etc., are inevitable. The performance of computer vision algorithms can significantly deteriorate when the training dataset is imbalanced. In recent years, Generative Adversarial Networks (GANs) have gained immense attention by researchers across a variety of application domains due to their capability to model complex real-world image data. It is particularly important that GANs can not only be used to generate synthetic images, but also its fascinating adversarial learning idea showed good potential in restoring balance in imbalanced datasets. In this paper, we examine the most recent developments of GANs based techniques for addressing imbalance problems in image data. The real-world challenges and implementations of synthetic image generation based on GANs are extensively covered in this survey. Our survey first introduces various imbalance problems in computer vision tasks and its existing solutions, and then examine key concepts such as deep generative image models and GANs. After that, we propose a taxonomy to summarize GANs based techniques for addressing imbalance problems in computer vision tasks into three major categories: 1. Image level imbalances in classification, 2. object level imbalances in object detection and 3. pixel level imbalances in segmentation tasks. We elaborate the imbalance problems of each group, and further provide GANs based solutions in each group. Readers will understand how GANs based techniques can handle the problem of imbalances and boost performance of the computer vision algorithms.

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“…Image segmentation that classifies every pixel in an image suffers from pixel level imbalances, as are other computer vision tasks.Some of the well-known image segmentation algorithms include Fully connected network [50], SegNet [51], U-Net [52], ResUNet [53] etc. Image segmentation is essential for a variety of tasks, including: Urban scene segmentation for autonomous driving [54], industrial inspection [55] and cancer cell segmentation [56]. Datasets of all these tasks suffer from pixel level imbalance.…”

Section: Figure 1: Distribution Of Different Type Of Datasets (A) Datmentioning

confidence: 99%

A Survey on Generative Adversarial Networks for imbalance problems in computer vision tasks

Sampath

Maurtua

Martín

et al. 2020

Preprint

View full text Add to dashboard Cite

Any computer vision application development starts off by acquiring images and data, then preprocessing and pattern recognition steps to perform a task. When the acquired images are highly imbalanced and not adequate, the desired task may not be achievable. Unfortunately, the occurrence of imbalance problems in acquired image datasets in certain complex real-world problems such as anomaly detection, emotion recognition, medical image analysis, fraud detection, metallic surface defect detection, disaster prediction, etc., are inevitable. The performance of computer vision algorithms can significantly deteriorate when the training dataset is imbalanced. In recent years, Generative Adversarial Networks (GANs) have gained immense attention by researchers across a variety of application domains due to their capability to model complex real-world image data. It is particularly important that GANs can not only be used to generate synthetic images, but also its fascinating adversarial learning idea showed good potential in restoring balance in imbalanced datasets.In this paper, we examine the most recent developments of GANs based techniques for addressing imbalance problems in image data. The real-world challenges and implementations of synthetic image generation based on GANs are extensively covered in this survey. Our survey first introduces various imbalance problems in computer vision tasks and its existing solutions, and then examines key concepts such as deep generative image models and GANs. After that, we propose a taxonomy to summarize GANs based techniques for addressing imbalance problems in computer vision tasks into three major categories: 1. Image level imbalances in classification, 2. object level imbalances in object detection and 3. pixel level imbalances in segmentation tasks. We elaborate the imbalance problems of each group, and provide GANs based solutions in each group. Readers will understand how GANs based techniques can handle the problem of imbalances and boost performance of the computer vision algorithms.

show abstract

Deep learning approaches to biomedical image segmentation

Cited by 269 publications

References 22 publications

Early survey with bibliometric analysis on machine learning approaches in controlling COVID-19 outbreaks

Early survey with bibliometric analysis on machine learning approaches in controlling COVID-19 outbreaks

A Survey on Generative Adversarial Networks for imbalance problems in computer vision tasks

A Survey on Generative Adversarial Networks for imbalance problems in computer vision tasks

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