ISBN: 9781394245338 - deep learning applications in medical image segmentation: overview, approaches, and challenges (16 resultados)

Condición: new.

HRD. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Condición: New.

Condición: As New. Unread book in perfect condition.

Condición: New.

Condición: As New. Unread book in perfect condition.

Condición: New.

Condición: New. In English.

Hardback. Condición: New. New copy - Usually dispatched within 4 working days.

Condición: New. 2025. 1st Edition. hardcover. . . . . .

Condición: New.

Condición: New. 2025. 1st Edition. hardcover. . . . . . Books ship from the US and Ireland.

Buch. Condición: Neu. Neuware - 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. 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. …

Condición: New. Brand New! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 6-10 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.…

Hardcover. Condición: new. Hardcover. 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. Readers will also find: Analysis of deep learning models, including FCN, UNet, SegNet, Dee Lab, and many moreDetailed discussion of medical image segmentation divided by area, incorporating all major organs and organ systemsRecent deep learning advancements in segmenting brain tumors, retinal vessels, and inner ear structuresAnalyzes the effectiveness of deep learning models in segmenting lung fields for respiratory disease diagnosisExplores the application and benefits of Generative Adversarial Networks (GANs) in enhancing medical image segmentationIdentifies and discusses the key challenges faced in medical image segmentation using deep learning techniquesProvides 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Hardcover. Condición: Brand New. 304 pages. 6.00x0.75x9.00 inches. In Stock. This item is printed on demand.