Sharp U-Net: Depthwise convolutional network for biomedical image segmentation

Zunair, Hasib; Hamza, A. Ben

doi:10.1016/j.compbiomed.2021.104699

Cited by 186 publications

(86 citation statements)

References 30 publications

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“…Typically, the contracting path conforms to the design of a conventional convolutional network. It is comprised of the repeated application of two 3 × 3 convolutions (unpadded convolutions), each followed by a rectified linear unit (ReLU) and a 2 × 2 maximum pooling operation with stride 2 for downsampling [ 40 , 41 ]. However, the algorithm developed in this study was built as U-Net while the contracting path was updated to Res-Net152V2.…”

Section: Methodsmentioning

confidence: 99%

Identification of Early Esophageal Cancer by Semantic Segmentation

Fang

Mukundan

Tsao

et al. 2022

JPM

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Early detection of esophageal cancer has always been difficult, thereby reducing the overall five-year survival rate of patients. In this study, semantic segmentation was used to predict and label esophageal cancer in its early stages. U-Net was used as the basic artificial neural network along with Resnet to extract feature maps that will classify and predict the location of esophageal cancer. A total of 75 white-light images (WLI) and 90 narrow-band images (NBI) were used. These images were classified into three categories: normal, dysplasia, and squamous cell carcinoma. After labeling, the data were divided into a training set, verification set, and test set. The training set was approved by the encoder–decoder model to train the prediction model. Research results show that the average time of 111 ms is used to predict each image in the test set, and the evaluation method is calculated in pixel units. Sensitivity is measured based on the severity of the cancer. In addition, NBI has higher accuracy of 84.724% when compared with the 82.377% accuracy rate of WLI, thereby making it a suitable method to detect esophageal cancer using the algorithm developed in this study.

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Section: Methodsmentioning

confidence: 99%

Identification of Early Esophageal Cancer by Semantic Segmentation

Fang

Mukundan

Tsao

et al. 2022

JPM

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“…Rehman et al [ 85 ] modify the skip connections of the U-Net introducing a feature enhancer block that adds more detail to the extracted features, helping the architecture to identify small regions. In a similar way, Zunair and Ben [ 86 ] introduce a sharp block in each skip connection of the U-Net in order to prevent the fusion of different features through the encoder convolution process and its merge with the decoder blocks. The sharpening process is performed by high pass kernels that infuse more relevance to distinct features.…”

Section: Generative Modelsmentioning

confidence: 99%

A survey on deep learning applied to medical images: from simple artificial neural networks to generative models

Celard

Iglesias

Sorribes-Fdez

et al. 2022

Neural Comput & Applic

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Deep learning techniques, in particular generative models, have taken on great importance in medical image analysis. This paper surveys fundamental deep learning concepts related to medical image generation. It provides concise overviews of studies which use some of the latest state-of-the-art models from last years applied to medical images of different injured body areas or organs that have a disease associated with (e.g., brain tumor and COVID-19 lungs pneumonia). The motivation for this study is to offer a comprehensive overview of artificial neural networks (NNs) and deep generative models in medical imaging, so more groups and authors that are not familiar with deep learning take into consideration its use in medicine works. We review the use of generative models, such as generative adversarial networks and variational autoencoders, as techniques to achieve semantic segmentation, data augmentation, and better classification algorithms, among other purposes. In addition, a collection of widely used public medical datasets containing magnetic resonance (MR) images, computed tomography (CT) scans, and common pictures is presented. Finally, we feature a summary of the current state of generative models in medical image including key features, current challenges, and future research paths.

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“…However, quantitative analysis of EAT is obtained by manual measurement, which is very onerous. Commandeur et al (80) proposed a fully automated quantitative tool to rapidly identify the pericardium and segment the epicardial and thoracic adipose tissues (TAT) from coronary calcium CT. Its results were more prominent compared to the improved version of the CNN with slice classification supervision (81). Commandeur combined two CNNs; they first segmented the heart and adipose tissues by a multitask CNN and then combined CNN and Statistical Shape Model (SSM) to detect the pericardium.…”

Section: Epicardial Adipose Tissue and Perivascular Adipose Tissuementioning

confidence: 99%

Artificial Intelligence in Coronary CT Angiography: Current Status and Future Prospects

Liao

Huang

et al. 2022

Front. Cardiovasc. Med.

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Coronary heart disease (CHD) is the leading cause of mortality in the world. Early detection and treatment of CHD are crucial. Currently, coronary CT angiography (CCTA) has been the prior choice for CHD screening and diagnosis, but it cannot meet the clinical needs in terms of examination quality, the accuracy of reporting, and the accuracy of prognosis analysis. In recent years, artificial intelligence (AI) has developed rapidly in the field of medicine; it played a key role in auxiliary diagnosis, disease mechanism analysis, and prognosis assessment, including a series of studies related to CHD. In this article, the application and research status of AI in CCTA were summarized and the prospects of this field were also described.

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Sharp U-Net: Depthwise convolutional network for biomedical image segmentation

Cited by 186 publications

References 30 publications

Identification of Early Esophageal Cancer by Semantic Segmentation

Identification of Early Esophageal Cancer by Semantic Segmentation

A survey on deep learning applied to medical images: from simple artificial neural networks to generative models

Artificial Intelligence in Coronary CT Angiography: Current Status and Future Prospects

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