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Generative Machine Learning Models in Medical Image Computing

Generative Machine Learning Models in Medical Image Computing" provides a comprehensive exploration of generative modeling techniques tailored to the unique demands of medical imaging. This book presents an in-depth overview of cutting-edge generative models such as GANs, VAEs, and diffusion mo...

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Bibliografiske detaljer
Institution som forfatter: SpringerLink (Online service)
Andre forfattere: Zhang, Le (Editor), Chen, Chen (Editor), Li, Zeju (Editor), Slabaugh, Greg (Editor)
Format: Electronisk eBog
Sprog:engelsk
Udgivet: Cham : Springer Nature Switzerland : Imprint: Springer, 2025.
Udgivelse:1st ed. 2025.
Fag:
Machine learning.
Medical informatics.
Image processing.
Machine Learning.
Health Informatics.
Image Processing.
Online adgang:https://doi.org/10.1007/978-3-031-80965-1
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Indholdsfortegnelse:
  • Part I Segmentation
  • Synthesis of annotated data for medical image segmentation
  • Diffusion Models For Histopathological Image Generation
  • Generative AI Techniques for Ultrasound Image Reconstruction
  • Part II Detection and Classification
  • Vision Language Pre training from Synthetic Data
  • Diffusion models for inverse problems in medical imaging
  • Virtual Elastography Ultrasound via Generative Adversarial Network and its Application to Breast Cancer Diagnosis
  • Generative Adversarial Networks for Brain MR Image Synthesis and Its Clinical Validation on Multiple Sclerosis
  • Histopathological Synthetic Augmentation with Generative Models
  • Part III Image Super resolution and Reconstruction
  • Enhancing PET with Image Generation Techniques Generating Standard dose PET from Low dose PET
  • EyesGAN Synthesize human face from human eyes
  • Deep Generative Models for 3D Medical Image Synthesis
  • Part IV Various Applications
  • Cross Modal Attention Fusion based Generative Adversarial Network for text to image synthesis
  • CHeart A Conditional Spatio Temporal Generative Model for Cardiac Anatomy
  • Generative Models for Synthesizing Anatomical Plausible 3D Medical Images
  • Diffusion Probabilistic Models for Image Formation in MRI
  • Embedding 3D CT Prior into X ray Imaging Using Generative Adversarial Networks.

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