Deep Learning and Convolutional Neural Networks for Medical Image Computing: Precision Medicine, High Performance and Large-Scale Datasets (Hardcover, 2017)

· 제목 : Deep Learning and Convolutional Neural Networks for Medical Image Computing: Precision Medicine, High Performance and Large-Scale Datasets (Hardcover, 2017) 
· 분류 : 외국도서 > 컴퓨터 > 컴퓨터 그래픽
· ISBN : 9783319429984
· 쪽수 : 326쪽
· 출판일 : 2017-07-24

Part I: Review

1.       Deep Learning and Computer-Aided Diagnosis for Medical Image Processing: A Personal Perspective
Ronald M. Summers

2.       Review of Deep Learning Methods in Mammography, Cardiovascular and Microscopy Image Analysis
Gustavo Carneiro, Yefeng Zheng, Fuyong Xing, and Lin Yang

·         Part II: Detection and Localization

3.       Efficient False-Positive Reduction in Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation
Holger R. Roth, Le Lu, Jiamin Liu, Jianhua Yao, Ari Seff, Kevin Cherry, Lauren Kim, and Ronald M. Summers

4.       Robust Landmark Detection in Volumetric Data with Efficient 3D Deep Learning
Yefeng Zheng, David Liu, Bogdan Georgescu, Hien Nguyen, and Dorin Comaniciu

5.       A Novel Cell Detection Method Using Deep Convolutional Neural Network and Maximum-Weight Independent Set
Fujun Liu and Lin Yang

6.       Deep Learning for Histopathological Image Analysis: Towards Computerized Diagnosis on Cancers
Jun Xu, Chao Zhou, Bing Lang, and Qingshan Liu

7.       Interstitial Lung Diseases via Deep Convolutional Neural Networks: Segmentation Label Propagation, Unordered Pooling and Cross-Dataset Learning
Mingchen Gao, Ziyue Xu, Le Lu, and Daniel J. Mollura

8.       Three Aspects on Using Convolutional Neural Networks for Computer-Aided Detection in Medical Imaging
Hoo-Chang Shin, Holger R. Roth, Mingchen Gao, Le Lu, Ziyue Xu, Isabella Nogues, Jianhua Yao, Daniel Mollura, and Ronald M. Summers

9.       Cell Detection with Deep Learning Accelerated by Sparse Kernel
Junzhou Huang and Zheng Xu

10.   Fully Convolutional Networks in Medical Imaging: Applications to Image Enhancement and Recognition
Christian Baumgartner, Ozan Oktay, and Daniel Rueckert

11.   On the Necessity of Fine-Tuned Convolutional Neural Networks for Medical Imaging
Nima Tajbakhsh, Jae Y. Shin, Suryakanth R. Gurudu, R. Todd Hurst, Christopher B. Kendall, Michael B. Gotway, and Jianming Liang

·         Part III: Segmentation

12.   Fully Automated Segmentation Using Distance Regularized Level Set and Deep-Structured Learning and Inference
Tuan Anh Ngo and Gustavo Carneiro

13.   Combining Deep Learning and Structured Prediction for Segmenting Masses in Mammograms
Neeraj Dhungel, Gustavo Carneiro, and Andrew P. Bradley

14.   Deep Learning Based Automatic Segmentation of Pathological Kidney in CT: Local vs. Global Image Context
Yefeng Zheng, David Liu, Bogdan Georgescu, Daguang Xu, and Dorin Comaniciu

15.   Robust Cell Detection and Segmentation in Histopathological Images using Sparse Reconstruction and Stacked Denoising Autoencoders
Hai Su, Fuyong Xing, Xiangfei Kong, Yuanpu Xie, Shaoting Zhang and Lin Yang

16.   Automatic Pancreas Segmentation Using Coarse-to-Fine Superpixel Labeling
Amal Farag, Le Lu, Holger R. Roth, Jiamin Liu, Evrim Turkbey, and Ronald M. Summers

·         Part IV: Big Dataset and Text-Image Deep Mining

17.   Interleaved Text/Image Deep Mining on a Large-Scale Radiology Image Database
Hoo-Chang Shin, Le Lu, Lauren Kim, Ari Seff, Jianhua Yao, and Ronald Summers