Deep Learning Applications in Medical Image Segmentation: Overview, Approaches, and ChallengesКНИГИ » ПРОГРАММИНГ
Название: Deep Learning Applications in Medical Image Segmentation: Overview, Approaches, and Challenges Автор: Sajid Yousuf Bhat, Aasia Rehman, Muhammad Abulaish Издательство: Wiley-IEEE Press Год: 2025 Страниц: 309 Язык: английский Формат: pdf (true) Размер: 10.1 MB
Apply revolutionary Deep Learning technology to the fast-growing field of medical image segmentation.
Precise medical image segmentation is rapidly becoming one of the most important tools in medical research, diagnosis, and treatment. The potential for Deep Learning, a technology which is already revolutionizing practice across hundreds of subfields, is immense. The prospect of using Deep Learning to address the traditional shortcomings of image segmentation demands close inspection and wide proliferation of relevant knowledge.
Deep Learning Applications in Medical Image Segmentation meets this demand with a comprehensive introduction and its growing applications. Covering foundational concepts and its advanced techniques, it offers a one-stop resource for researchers and other readers looking for a detailed understanding of the topic. It is deeply engaged with the main challenges and recent advances in the field of deep-learning-based medical image segmentation.
The book also covers cutting-edge topics such as the use of generative adversarial networks (GANs) for image segmentation and collaborative models for cell image segmentation. By including these advanced topics, we aim to provide readers with a forward-looking perspective on the future of medical image segmentation.
CNNs are sophisticated neural network architectures designed to analyze two-dimensional (2D) images. However, it is applicable to both one-dimensional (1D) and three-dimensional (3D) data. The core of any CNN system is the convolutional layer. Convolution, like a classic neural network, is a linear operation where weights are multiplied by the input. The data that is being input is given a linear transformation by having a 2D array of weights, also known as a mask or filter, applied to it. The size of this filter is less than that of the original data. One of CNN’s main benefits is that its filters do not have to be designed by hand. Instead, they may be learned automatically by back-propagation training, in which the reverse pass employs the convolution operation with spatially inverted filters. Stacking CNN’s convolutional layers is a highly effective technique.
Readers will also find: Analysis of Deep Learning models, including FCN, UNet, SegNet, Dee Lab, and many more Detailed discussion of medical image segmentation divided by area, incorporating all major organs and organ systems Recent deep learning advancements in segmenting brain tumors, retinal vessels, and inner ear structures Analyzes the effectiveness of deep learning models in segmenting lung fields for respiratory disease diagnosis Explores the application and benefits of Generative Adversarial Networks (GANs) in enhancing medical image segmentation Identifies and discusses the key challenges faced in medical image segmentation using Deep Learning techniques Provides an overview of the latest advancements, applications, and future trends in Deep Learning for medical image analysis
Deep Learning Applications in Medical Image Segmentation is ideal for academics and researchers working with medical image segmentation, as well as professionals in medical imaging, Data Science, and biomedical engineering.
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Deep Learning for Medical Image Analysis Название: Deep Learning for Medical Image Analysis Автор: S. Kevin Zhou Издательство: Academic Press Год: 2017 Страниц: 458 Формат: True PDF Размер:...
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Deep Learning Applications in Medical Image Segmentation: Overview, Approaches, and Challenges 
