Dr. Le Lu is a Staff Scientist in the Radiology and Imaging Sciences Department of the National Institutes of Health Clinical Center, Bethesda, MD, USA.Dr. Yefeng Zheng is a Senior Staff Scientist at Siemens Healthcare Technology Center, Princeton, NJ, USA.Dr. Gustavo Carneiro is an Associate Professor in the School of Computer Science at The University of Adelaide, Australia.Dr. Lin Yang is an Associate Professor in the Department of Biomedical Engineering at the University of Florida, Gainesville, FL, USA.

“This book … is very suitable for students, researchers and practitioner. In addition, the book provides an important and useful reference for experienced researchers on particular aspects of deep learning based medical image analysis.” (Guang Yang, IAPR Newsletter, Vol. 41 (2), April, 2019)

• 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 RadiologyImage Database.

Dr. Le Lu is a Staff Scientist in the Radiology and Imaging Sciences Department of the National Institutes of Health Clinical Center, Bethesda, MD, USA.Dr. Yefeng Zheng is a Senior Staff Scientist at Siemens Healthcare Technology Center, Princeton, NJ, USA.Dr. Gustavo Carneiro is an Associate Professor in the School of Computer Science at The University of Adelaide, Australia.Dr. Lin Yang is an Associate Professor in the Department of Biomedical Engineering at the University of Florida, Gainesville, FL, USA.

“This book … is very suitable for students, researchers and practitioner. In addition, the book provides an important and useful reference for experienced researchers on particular aspects of deep learning based medical image analysis.” (Guang Yang, IAPR Newsletter, Vol. 41 (2), April, 2019)

• 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 RadiologyImage Database.

Dr. Le Lu is a Staff Scientist in the Radiology and Imaging Sciences Department of the National Institutes of Health Clinical Center, Bethesda, MD, USA.Dr. Yefeng Zheng is a Senior Staff Scientist at Siemens Healthcare Technology Center, Princeton, NJ, USA.Dr. Gustavo Carneiro is an Associate Professor in the School of Computer Science at The University of Adelaide, Australia.Dr. Lin Yang is an Associate Professor in the Department of Biomedical Engineering at the University of Florida, Gainesville, FL, USA.

“This book … is very suitable for students, researchers and practitioner. In addition, the book provides an important and useful reference for experienced researchers on particular aspects of deep learning based medical image analysis.” (Guang Yang, IAPR Newsletter, Vol. 41 (2), April, 2019)

• 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 RadiologyImage Database.