Deep Learning and Convolutional Neural Networks for Medical Image Computing : Precision Medicine, High Performance and Large-Scale Datasets /

Deep Learning and Convolutional Neural Networks for Medical Image Computing : Precision Medicine, High Performance and Large-Scale Datasets /

Detalles Bibliográficos
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Lu, Le. (Editor ), Zheng, Yefeng. (Editor ), Carneiro, Gustavo. (Editor ), Yang, Lin. (Editor )
Formato: eBook
Lenguaje:English
Publicado: Cham : Springer International Publishing : Imprint: Springer, 2017.
Edición:1st ed. 2017.
Colección:Advances in Computer Vision and Pattern Recognition,
Materias:
Optical data processing.
Artificial intelligence.
Neural networks (Computer science) .
Radiology.
Image Processing and Computer Vision.
Artificial Intelligence.
Mathematical Models of Cognitive Processes and Neural Networks.
Imaging / Radiology.
Tabla de Contenidos:
  • Part I: Review
  • Chapter 1. Deep Learning and Computer-Aided Diagnosis for Medical Image Processing: A Personal Perspective
  • Chapter 2. Review of Deep Learning Methods in Mammography, Cardiovascular and Microscopy Image Analysis
  • Part II: Detection and Localization
  • Chapter 3. Efficient False-Positive Reduction in Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation
  • Chapter 4. Robust Landmark Detection in Volumetric Data with Efficient 3D Deep Learning
  • Chapter 5. A Novel Cell Detection Method Using Deep Convolutional Neural Network and Maximum-Weight Independent Set
  • Chapter 6. Deep Learning for Histopathological Image Analysis: Towards Computerized Diagnosis on Cancers
  • Chapter 7. Interstitial Lung Diseases via Deep Convolutional Neural Networks: Segmentation Label Propagation, Unordered Pooling and Cross-Dataset Learning
  • Chapter 8. Three Aspects on Using Convolutional Neural Networks for Computer-Aided Detection in Medical Imaging
  • Chapter 9. Cell Detection with Deep Learning Accelerated by Sparse Kernel
  • Chapter 10. Fully Convolutional Networks in Medical Imaging: Applications to Image Enhancement and Recognition
  • Chapter 11. On the Necessity of Fine-Tuned Convolutional Neural Networks for Medical Imaging
  • Part III: Segmentation
  • Chapter 12. Fully Automated Segmentation Using Distance Regularized Level Set and Deep-Structured Learning and Inference
  • Chapter 13. Combining Deep Learning and Structured Prediction for Segmenting Masses in Mammograms
  • Chapter 14. Deep Learning Based Automatic Segmentation of Pathological Kidney in CT: Local vs. Global Image Context
  • Chapter 15. Robust Cell Detection and Segmentation in Histopathological Images using Sparse Reconstruction and Stacked Denoising Autoencoders
  • Chapter 16. Automatic Pancreas Segmentation Using Coarse-to-Fine Superpixel Labeling
  • Part IV: Big Dataset and Text-Image Deep Mining
  • Chapter 17. Interleaved Text/Image Deep Mining on a Large-Scale Radiology Image Database.

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