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ORCIDDLMIA/ML-CDS@MICCAI 2017: Quebec City, QC, Canada
- M. Jorge Cardoso, Tal Arbel, Gustavo Carneiro, Tanveer F. Syeda-Mahmood, João Manuel R. S. Tavares, Mehdi Moradi, Andrew P. Bradley, Hayit Greenspan, João Paulo Papa, Anant Madabhushi, Jacinto C. Nascimento, Jaime S. Cardoso, Vasileios Belagiannis, Zhi Lu:
Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support - Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, 2017, Proceedings. Lecture Notes in Computer Science 10553, Springer 2017, ISBN 978-3-319-67557-2
Third International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2017
- Abhay Shah, Michael D. Abràmoff
, Xiaodong Wu:
Simultaneous Multiple Surface Segmentation Using Deep Learning. 3-11 - Yuexiang Li, Linlin Shen:
A Deep Residual Inception Network for HEp-2 Cell Classification. 12-20 - Roger Trullo, Caroline Petitjean, Dong Nie, Dinggang Shen, Su Ruan:
Joint Segmentation of Multiple Thoracic Organs in CT Images with Two Collaborative Deep Architectures. 21-29 - Ohad Shitrit, Tammy Riklin Raviv:
Accelerated Magnetic Resonance Imaging by Adversarial Neural Network. 30-38 - Honghui Liu, Jianjiang Feng, Zishun Feng, Jiwen Lu, Jie Zhou:
Left Atrium Segmentation in CT Volumes with Fully Convolutional Networks. 39-46 - Siqi Bao, Pei Wang, Albert C. S. Chung:
3D Randomized Connection Network with Graph-Based Inference. 47-55 - Pim Moeskops, Mitko Veta, Maxime W. Lafarge, Koen A. J. Eppenhof, Josien P. W. Pluim:
Adversarial Training and Dilated Convolutions for Brain MRI Segmentation. 56-64 - Shekoufeh Gorgi Zadeh, Maximilian W. M. Wintergerst, Vitalis Wiens, Sarah Thiele, Frank G. Holz, Robert Patrick Finger, Thomas Schultz:
CNNs Enable Accurate and Fast Segmentation of Drusen in Optical Coherence Tomography. 65-73 - S. M. Masudur Rahman Al-Arif, Karen Knapp, Greg G. Slabaugh:
Region-Aware Deep Localization Framework for Cervical Vertebrae in X-Ray Images. 74-82 - Maxime W. Lafarge, Josien P. W. Pluim, Koen A. J. Eppenhof, Pim Moeskops, Mitko Veta:
Domain-Adversarial Neural Networks to Address the Appearance Variability of Histopathology Images. 83-91 - Sangheum Hwang, Sunggyun Park:
Accurate Lung Segmentation via Network-Wise Training of Convolutional Networks. 92-99 - Fatemeh Taheri Dezaki, Neeraj Dhungel, Amir H. Abdi, Christina Luong, Teresa Tsang, John Jue, Ken Gin, Dale Hawley, Robert Rohling, Purang Abolmaesumi:
Deep Residual Recurrent Neural Networks for Characterisation of Cardiac Cycle Phase from Echocardiograms. 100-108 - Matthieu Lê, Jesse Lieman-Sifry, Felix Lau, Sean Sall, Albert Hsiao, Daniel Golden:
Computationally Efficient Cardiac Views Projection Using 3D Convolutional Neural Networks. 109-116 - Yuru Pei, Yungeng Zhang, Haifang Qin, Gengyu Ma, Yuke Guo, Tianmin Xu, Hongbin Zha:
Non-rigid Craniofacial 2D-3D Registration Using CNN-Based Regression. 117-125 - Min Tang, Sepehr Valipour, Zichen Zhang, Dana Cobzas, Martin Jägersand:
A Deep Level Set Method for Image Segmentation. 126-134 - Daniel Bug, Steffen Schneider, Anne Grote, Eva Oswald, Friedrich Feuerhake, Julia Schüler, Dorit Merhof:
Context-Based Normalization of Histological Stains Using Deep Convolutional Features. 135-142 - Shazia Akbar, Mohammad Peikari, Sherine Salama, Sharon Nofech-Mozes, Anne L. Martel:
Transitioning Between Convolutional and Fully Connected Layers in Neural Networks. 143-150 - Behrouz Saghafi, Prabhat Garg, Benjamin C. Wagner, S. Carrie Smith, Jianzhao Xu, Ananth J. Madhuranthakam, Youngkyoo Jung, Jasmin Divers, Barry I. Freedman, Joseph A. Maldjian, Albert Montillo:
Quantifying the Impact of Type 2 Diabetes on Brain Perfusion Using Deep Neural Networks. 151-159 - Kim-Han Thung, Pew-Thian Yap, Dinggang Shen:
Multi-stage Diagnosis of Alzheimer's Disease with Incomplete Multimodal Data via Multi-task Deep Learning. 160-168 - William Lotter, Greg Sorensen, David D. Cox:
A Multi-scale CNN and Curriculum Learning Strategy for Mammogram Classification. 169-177 - Dheeraj Mundhra, Bharath Cheluvaraju, Jaiprasad Rampure, Tathagato Rai Dastidar:
Analyzing Microscopic Images of Peripheral Blood Smear Using Deep Learning. 178-185 - Xiaowei Hu, Lequan Yu, Hao Chen, Jing Qin, Pheng-Ann Heng:
AGNet: Attention-Guided Network for Surgical Tool Presence Detection. 186-194 - Kevin George, Adam P. Harrison, Dakai Jin, Ziyue Xu, Daniel J. Mollura:
Pathological Pulmonary Lobe Segmentation from CT Images Using Progressive Holistically Nested Neural Networks and Random Walker. 195-203 - Bob D. de Vos, Floris F. Berendsen, Max A. Viergever, Marius Staring, Ivana Isgum:
End-to-End Unsupervised Deformable Image Registration with a Convolutional Neural Network. 204-212 - Azam Hamidinekoo, Reyer Zwiggelaar:
Stain Colour Normalisation to Improve Mitosis Detection on Breast Histology Images. 213-221 - Masahiro Oda, Natsuki Shimizu, Holger R. Roth, Kenichi Karasawa, Takayuki Kitasaka, Kazunari Misawa, Michitaka Fujiwara, Daniel Rueckert, Kensaku Mori:
3D FCN Feature Driven Regression Forest-Based Pancreas Localization and Segmentation. 222-230 - Kyunghyun Paeng, Sangheum Hwang, Sunggyun Park, Minsoo Kim:
A Unified Framework for Tumor Proliferation Score Prediction in Breast Histopathology. 231-239 - Carole H. Sudre, Wenqi Li, Tom Vercauteren, Sébastien Ourselin, M. Jorge Cardoso:
Generalised Dice Overlap as a Deep Learning Loss Function for Highly Unbalanced Segmentations. 240-248 - Inwan Yoo, David G. C. Hildebrand, Willie F. Tobin, Wei-Chung Allen Lee, Won-Ki Jeong:
ssEMnet: Serial-Section Electron Microscopy Image Registration Using a Spatial Transformer Network with Learned Features. 249-257 - Michal Sofka, Fausto Milletari, Jimmy Jia, Alex Rothberg:
Fully Convolutional Regression Network for Accurate Detection of Measurement Points. 258-266 - Zhipeng Ding, Greg M. Fleishman, Xiao Yang, Paul Thompson, Roland Kwitt, Marc Niethammer:
Fast Predictive Simple Geodesic Regression. 267-275 - Arijit Patra, Weilin Huang, J. Alison Noble:
Learning Spatio-Temporal Aggregation for Fetal Heart Analysis in Ultrasound Video. 276-284 - Aleksander Klibisz, Derek C. Rose, Matthew Eicholtz, Jay Blundon, Stanislav Zakharenko:
Fast, Simple Calcium Imaging Segmentation with Fully Convolutional Networks. 285-293 - Amir Jamaludin, Timor Kadir, Andrew Zisserman:
Self-supervised Learning for Spinal MRIs. 294-302 - Xinzi He, Zhen Yu, Tianfu Wang, Baiying Lei:
Skin Lesion Segmentation via Deep RefineNet. 303-311 - Jie Wei, Yong Xia:
Multi-scale Networks for Segmentation of Brain Magnetic Resonance Images. 312-320 - Ayelet Akselrod-Ballin, Leonid Karlinsky, Alon Hazan, Ran Bakalo, Ami Ben Horesh, Yoel Shoshan, Ella Barkan:
Deep Learning for Automatic Detection of Abnormal Findings in Breast Mammography. 321-329 - Adam Porisky, Tom Brosch, Emil Ljungberg, Lisa Y. W. Tang, Youngjin Yoo, Benjamin De Leener, Anthony Traboulsee, Julien Cohen-Adad, Roger C. Tam:
Grey Matter Segmentation in Spinal Cord MRIs via 3D Convolutional Encoder Networks with Shortcut Connections. 330-337
7th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2017
- Johannes Hofmanninger, Bjoern H. Menze, Marc-André Weber, Georg Langs:
Mapping Multi-Modal Routine Imaging Data to a Single Reference via Multiple Templates. 341-348 - Ravnoor S. Gill, Seok-Jun Hong, Fatemeh Fadaie, Benoît Caldairou, Boris C. Bernhardt, Neda Bernasconi, Andrea Bernasconi:
Automated Detection of Epileptogenic Cortical Malformations Using Multimodal MRI. 349-356 - Manon Ansart, Stéphane Epelbaum, Geoffroy Gagliardi, Olivier Colliot, Didier Dormont, Bruno Dubois, Harald Hampel, Stanley Durrleman:
Prediction of Amyloidosis from Neuropsychological and MRI Data for Cost Effective Inclusion of Pre-symptomatic Subjects in Clinical Trials. 357-364 - Torsten Hopp, P. Cotic Smole, Nicole V. Ruiter:
Automated Multimodal Breast CAD Based on Registration of MRI and Two View Mammography. 365-372 - Shikha Chaganti, Jamie R. Robinson, Camilo Bermudez, Thomas A. Lasko, Louise A. Mawn, Bennett A. Landman:
EMR-Radiological Phenotypes in Diseases of the Optic Nerve and Their Association with Visual Function. 373-381
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