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ORCIDDeep Learning and Convolutional Neural Networks for Medical Image Computing 2017
- Le Lu, Yefeng Zheng, Gustavo Carneiro, Lin Yang:
Deep Learning and Convolutional Neural Networks for Medical Image Computing - Precision Medicine, High Performance and Large-Scale Datasets. Advances in Computer Vision and Pattern Recognition, Springer 2017, ISBN 978-3-319-42998-4
Review
- Ronald M. Summers:
Deep Learning and Computer-Aided Diagnosis for Medical Image Processing: A Personal Perspective. 3-10 - Gustavo Carneiro
, Yefeng Zheng, Fuyong Xing, Lin Yang:
Review of Deep Learning Methods in Mammography, Cardiovascular, and Microscopy Image Analysis. 11-32
Detection and Localization
- Holger R. Roth, Le Lu, Jiamin Liu, Jianhua Yao, Ari Seff, Kevin M. Cherry, Lauren Kim, Ronald M. Summers:
Efficient False Positive Reduction in Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation. 35-48 - Yefeng Zheng, David Liu, Bogdan Georgescu, Hien Nguyen, Dorin Comaniciu:
Robust Landmark Detection in Volumetric Data with Efficient 3D Deep Learning. 49-61 - Fujun Liu, Lin Yang:
A Novel Cell Detection Method Using Deep Convolutional Neural Network and Maximum-Weight Independent Set. 63-72 - Jun Xu, Chao Zhou, Bing Lang, Qingshan Liu:
Deep Learning for Histopathological Image Analysis: Towards Computerized Diagnosis on Cancers. 73-95 - Mingchen Gao, Ziyue Xu, Daniel J. Mollura:
Interstitial Lung Diseases via Deep Convolutional Neural Networks: Segmentation Label Propagation, Unordered Pooling and Cross-Dataset Learning. 97-111 - Hoo-Chang Shin, Holger R. Roth, Mingchen Gao, Le Lu, Ziyue Xu, Isabella Nogues, Jianhua Yao, Daniel J. Mollura, Ronald M. Summers:
Three Aspects on Using Convolutional Neural Networks for Computer-Aided Detection in Medical Imaging. 113-136 - Junzhou Huang, Zheng Xu:
Cell Detection with Deep Learning Accelerated by Sparse Kernel. 137-157 - Christian F. Baumgartner, Ozan Oktay, Daniel Rueckert:
Fully Convolutional Networks in Medical Imaging: Applications to Image Enhancement and Recognition. 159-179 - Nima Tajbakhsh, Jae Y. Shin, Suryakanth R. Gurudu, R. Todd Hurst, Christopher B. Kendall, Michael B. Gotway, Jianming Liang:
On the Necessity of Fine-Tuned Convolutional Neural Networks for Medical Imaging. 181-193
Segmentation
- Tuan Anh Ngo, Gustavo Carneiro:
Fully Automated Segmentation Using Distance Regularised Level Set and Deep-Structured Learning and Inference. 197-224 - Neeraj Dhungel, Gustavo Carneiro, Andrew P. Bradley:
Combining Deep Learning and Structured Prediction for Segmenting Masses in Mammograms. 225-240 - Yefeng Zheng, David Liu, Bogdan Georgescu, Daguang Xu, Dorin Comaniciu:
Deep Learning Based Automatic Segmentation of Pathological Kidney in CT: Local Versus Global Image Context. 241-255 - Hai Su, Fuyong Xing, Xiangfei Kong, Yuanpu Xie, Shaoting Zhang, Lin Yang:
Robust Cell Detection and Segmentation in Histopathological Images Using Sparse Reconstruction and Stacked Denoising Autoencoders. 257-278 - Amal Farag, Le Lu, Holger R. Roth, Jiamin Liu, Evrim Turkbey, Ronald M. Summers:
Automatic Pancreas Segmentation Using Coarse-to-Fine Superpixel Labeling. 279-302
Big Dataset and Text-Image Deep Mining
- Hoo-Chang Shin, Le Lu, Lauren Kim, Ari Seff, Jianhua Yao, Ronald M. Summers:
Interleaved Text/Image Deep Mining on a Large-Scale Radiology Image Database. 305-321
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