Deep Learning and Convolutional Neural Networks for Medical Image Computing

Part I: Review <p>1.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Deep Learning and Computer-Aided Diagnosis for Medical Image Processing: A Personal Perspective<br> Ronald M. Summers</p>

<p>2.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Review of Deep Learning Methods in Mammography, Cardiovascular and Microscopy Image Analysis<br>Gustavo Carneiro, Yefeng Zheng, Fuyong Xing, and Lin Yang</p>

·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Part II: Detection and Localization<p></p>

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

<p>4.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Robust Landmark Detection in Volumetric Data with Efficient 3D Deep Learning<br> Yefeng Zheng, David Liu, Bogdan Georgescu, Hien Nguyen, and Dorin Comaniciu</p>

<p>5.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; A Novel Cell Detection Method Using Deep Convolutional Neural Network and Maximum-Weight Independent Set<br> Fujun Liu and Lin Yang</p>

<p>6.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Deep Learning for Histopathological Image Analysis: Towards Computerized Diagnosis on Cancers<br> Jun Xu, Chao Zhou, Bing Lang, and Qingshan Liu</p>

<p>7.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Interstitial Lung Diseases via Deep Convolutional Neural Networks: Segmentation Label Propagation, Unordered Pooling and Cross-Dataset Learning<br> Mingchen Gao, Ziyue Xu, Le Lu, and Daniel J. Mollura</p>

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

<p>9.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Cell Detection with Deep Learning Accelerated by Sparse Kernel<br> Junzhou Huang and Zheng Xu</p>

<p>10.&nbsp;&nbsp; Fully Convolutional Networks in Medical Imaging: Applications to Image Enhancement and Recognition<br> Christian Baumgartner, Ozan Oktay, and Daniel Rueckert</p>

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

<p>·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Part III: Segmentation</p>

12.&nbsp;&nbsp; Fully Automated Segmentation Using Distance Regularized Level Set and Deep-Structured Learning and Inference<br> Tuan Anh Ngo and Gustavo Carneiro<p></p>

<p>13.&nbsp;&nbsp; Combining Deep Learning and Structured Prediction for Segmenting Masses in Mammograms<br> Neeraj Dhungel, Gustavo Carneiro, and Andrew P. Bradley</p>

<p>14.&nbsp;&nbsp; Deep Learning Based Automatic Segmentation of Pathological Kidney in CT: Local vs. Global Image Context<br> Yefeng Zheng, David Liu, Bogdan Georgescu, Daguang Xu, and Dorin Comaniciu</p>

<p>15.&nbsp;&nbsp; Robust Cell Detection and Segmentation in Histopathological Images using Sparse Reconstruction and Stacked Denoising Autoencoders<br> Hai Su, Fuyong Xing, Xiangfei Kong, Yuanpu Xie, Shaoting Zhang and Lin Yang</p>

16.&nbsp;&nbsp; Automatic Pancreas Segmentation Using Coarse-to-Fine Superpixel Labeling<br> Amal Farag, Le Lu, Holger R. Roth, Jiamin Liu, Evrim Turkbey, and Ronald M. Summers<p></p>

<p>·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Part IV: Big Dataset and Text-Image Deep Mining</p>

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