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تسجيل مستخدم جديدModality specific U-Net variants for biomedical image segmentation: A survey
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With the advent of advancements in deep learning approaches, such as deep convolution neural network, residual neural network, adversarial network; U-Net architectures are most widely utilized in biomedical image segmentation to address the automation in identification and detection of the target regions or sub-regions. In recent studies, U-Net based approaches have illustrated state-of-the-art performance in different applications for the development of computer-aided diagnosis systems for early diagnosis and treatment of diseases such as brain tumor, lung cancer, alzheimer, breast cancer, etc. This article contributes to present the success of these approaches by describing the U-Net framework, followed by the comprehensive analysis of the U-Net variants for different medical imaging or modalities such as magnetic resonance imaging, X-ray, computerized tomography/computerized axial tomography, ultrasound, positron emission tomography, etc. Besides, this article also highlights the contribution of U-Net based frameworks in the on-going pandemic, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) also known as COVID-19.
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Methods based on convolutional neural networks have improved the performance of biomedical image segmentation. However, most of these methods cannot efficiently segment objects of variable sizes and train on small and biased datasets, which are commo
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Most methods for medical image segmentation use U-Net or its variants as they have been successful in most of the applications. After a detailed analysis of these traditional encoder-decoder based approaches, we observed that they perform poorly in d
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Fine-tuning a network which has been trained on a large dataset is an alternative to full training in order to overcome the problem of scarce and expensive data in medical applications. While the shallow layers of the network are usually kept unchang
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