Deep Learning Models for Medical Imaging

Deep Learning is a significant methodology in medical image analysis. Deep Learning Models for Medical Imaging presents deep learning concepts and modeling as applied to medical imaging and/or healthcare, using two different real-world case studies. It provides a complete implementation (via GitHub) of both standard (e.g. LeNet, Alexnet, VGGNet, ResNet and InceptionNet) and recent models (Mobile net and squeeze-and excitation net). Deep Learning Models for Medical Imaging is suitable for computer science, medical imaging and biomedical engineering researchers and students who need up-to-date deep learning tools to apply to medical image analysis problems. Provides a step-by-step approach to develop deep learning modelsPresents case studies showing end to end implementationCodes are provided in GitHub INDICE: 1. Introduction2. Deep learning: A review3. Deep learning models4. Case study I: Histopathology images5. Case study II: Chest radiographs6. Other medical imaging issues7. Conclusion

• ISBN: 978-0-12-823504-1
• Editorial: Academic Press
• Encuadernacion: Rústica
• Páginas: 180
• Fecha Publicación: 01/06/2021
• Nº Volúmenes: 1
• Idioma: Inglés