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• Third International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2017:
• Simultaneous Multiple Surface Segmentation Using Deep Learning / Abhay Shah, Michael D. Abramoff, Xiaodong Wu
• A Deep Residual Inception Network for HEp-2 Cell Classification / Yuexiang Li, Linlin Shen
• Joint Segmentation of Multiple Thoracic Organs in CT Images with Two Collaborative Deep Architectures / Roger Trullo, Caroline Petitjean, Dong Nie, Dinggang Shen, Su Ruan
• Accelerated Magnetic Resonance Imaging by Adversarial Neural Network / Ohad Shitrit, Tammy Riklin Raviv
• Left Atrium Segmentation in CT Volumes with Fully Convolutional Networks / Honghui Liu, Jianjiang Feng, Zishun Feng, Jiwen Lu, Jie Zhou
• 3D Randomized Connection Network with Graph-Based Inference / Siqi Bao, Pei Wang, Albert C. S. Chung
• Adversarial Training and Dilated Convolutions for Brain MRI Segmentation / Pim Moeskops, Mitko Veta, Maxime W. Lafarge, Koen A. J. Eppenhof, Josien P. W. Pluim
• CNNs Enable Accurate and Fast Segmentation of Drusen in Optical Coherence Tomography / Shekoufeh Gorgi Zadeh, Maximilian W. M. Wintergerst, Vitalis Wiens, Sarah Thiele, Frank G. Holz, Robert P. Finger et al.
• Region-Aware Deep Localization Framework for Cervical Vertebrae in X-Ray Images / S. M. Masudur Rahman Al Arif, Karen Knapp, Greg Slabaugh
• Domain-Adversarial Neural Networks to Address the Appearance Variability of Histopathology Images / Maxime W. Lafarge, Josien P. W. Pluim, Koen A. J. Eppenhof, Pim Moeskops, Mitko Veta
• Accurate Lung Segmentation via Network-Wise Training of Convolutional Networks / Sangheum Hwang, Sunggyun Park
• Deep Residual Recurrent Neural Networks for Characterisation of Cardiac Cycle Phase from Echocardiograms / Fatemeh Taheri Dezaki, Neeraj Dhungel, Amir H. Abdi, Christina Luong, Teresa Tsang, John Jue et al.
• Computationally Efficient Cardiac Views Projection Using 3D Convolutional Neural Networks / Matthieu Le, Jesse Lieman-Sifry, Felix Lau, Sean Sall, Albert Hsiao, Daniel Golden
• Non-rigid Craniofacial 2D-3D Registration Using CNN-Based Regression / Yuru Pei, Yungeng Zhang, Haifang Qin, Gengyu Ma, Yuke Guo, Tianmin Xu et al.
• A Deep Level Set Method for Image Segmentation / Min Tang, Sepehr Valipour, Zichen Zhang, Dana Cobzas, Martin Jagersand
• Context-Based Normalization of Histological Stains Using Deep Convolutional Features / D. Bug, S. Schneider, A. Grote, E. Oswald, F. Feuerhake, J. Schüler et al.
• Transitioning Between Convolutional and Fully Connected Layers in Neural Networks / Shazia Akbar, Mohammad Peikari, Sherine Salama, Sharon Nofech-Mozes, Anne Martel
• Quantifying the Impact of Type 2 Diabetes on Brain Perfusion Using Deep Neural Networks / Behrouz Saghafi, Prabhat Garg, Benjamin C. Wagner, S. Carrie Smith, Jianzhao Xu, Ananth J. Madhuranthakam et al.
• Multi-stage Diagnosis of Alzheimer's Disease with Incomplete Multimodal Data via Multi-task Deep Learning / Kim-Han Thung, Pew-Thian Yap, Dinggang Shen
• A Multi-scale CNN and Curriculum Learning Strategy for Mammogram Classification / William Lotter, Greg Sorensen, David Cox
• Analyzing Microscopic Images of Peripheral Blood Smear Using Deep Learning / Dheeraj Mundhra, Bharath Cheluvaraju, Jaiprasad Rampure, Tathagato Rai Dastidar
• AGNet: Attention-Guided Network for Surgical Tool Presence Detection / Xiaowei Hu, Lequan Yu, Hao Chen, Jing Qin, Pheng-Ann Heng
• Pathological Pulmonary Lobe Segmentation from CT Images Using Progressive Holistically Nested Neural Networks and Random Walker / Kevin George, Adam P. Harrison, Dakai Jin, Ziyue Xu, Daniel J. Mollura
• End-to-End Unsupervised Deformable Image Registration with a Convolutional Neural Network / Bob D. de Vos, Floris F. Berendsen, Max A. Viergever, Marius Staring, Ivana Išgum
• Stain Colour Normalisation to Improve Mitosis Detection on Breast Histology Images / Azam Hamidinekoo, Reyer Zwiggelaar
• 3D FCN Feature Driven Regression Forest-Based Pancreas Localization and Segmentation / Masahiro Oda, Natsuki Shimizu, Holger R. Roth, Ken'ichi Karasawa, Takayuki Kitasaka, Kazunari Misawa et al.
• A Unified Framework for Tumor Proliferation Score Prediction in Breast Histopathology / Kyunghyun Paeng, Sangheum Hwang, Sunggyun Park, Minsoo Kim
• Generalised Dice Overlap as a Deep Learning Loss Function for Highly Unbalanced Segmentations / Carole H. Sudre, Wenqi Li, Tom Vercauteren, Sebastien Ourselin, M. Jorge Cardoso
• ssEMnet: Serial-Section Electron Microscopy Image Registration Using a Spatial Transformer Network with Learned Features / Inwan Yoo, David G. C. Hildebrand, Willie F. Tobin, Wei-Chung Allen Lee, Won-Ki Jeong
• Fully Convolutional Regression Network for Accurate Detection of Measurement Points / Michal Sofka, Fausto Milletari, Jimmy Jia, Alex Rothberg
• Fast Predictive Simple Geodesic Regression / Zhipeng Ding, Greg Fleishman, Xiao Yang, Paul Thompson, Roland Kwitt, Marc Niethammer et al.
• Learning Spatio-Temporal Aggregation for Fetal Heart Analysis in Ultrasound Video / Arijit Patra, Weilin Huang, J. Alison Noble
• Fast, Simple Calcium Imaging Segmentation with Fully Convolutional Networks / Aleksander Klibisz, Derek Rose, Matthew Eicholtz, Jay Blundon, Stanislav Zakharenko
• Self-supervised Learning for Spinal MRIs / Amir Jamaludin, Timor Kadir, Andrew Zisserman
• Skin Lesion Segmentation via Deep RefineNet / Xinzi He, Zhen Yu, Tianfu Wang, Baiying Lei
• Multi-scale Networks for Segmentation of Brain Magnetic Resonance Images / Jie Wei, Yong Xia
• Deep Learning for Automatic Detection of Abnormal Findings in Breast Mammography / Ayelet Akselrod-Ballin, Leonid. Karlinsky, Alon Hazan, Ran Bakalo, Ami Ben Horesh, Yoel Shoshan et al.
• Grey Matter Segmentation in Spinal Cord MRIs via 3D Convolutional Encoder Networks with Shortcut Connections / Adam Porisky, Tom Brosch, Emil Ljungberg, Lisa Y. W. Tang, Youngjin Yoo, Benjamin De Leener et al.
• 7th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2017:
• Mapping Multi-Modal Routine Imaging Data to a Single Reference via Multiple Templates / Johannes Hofmanninger, Bjoern Menze, Marc-André Weber, Georg Langs
• Automated Detection of Epileptogenic Cortical Malformations Using Multimodal MRI / Ravnoor S. Gill, Seok-Jun Hong, Fatemeh Fadaie, Benoit Caldairou, Boris Bernhardt, Neda Bernasconi et al.
• Prediction of Amyloidosis from Neuropsychological and MRI Data for Cost Effective Inclusion of Pre-symptomatic Subjects in Clinical Trials / Manon Ansart, Stéphane Epelbaum, Geoffroy Gagliardi, Olivier Colliot, Didier Dormont, Bruno Dubois et al.
• Automated Multimodal Breast CAD Based on Registration of MRI and Two View Mammography / T. Hopp, P. Cotic Smole, N. V. Ruiter
• EMR-Radiological Phenotypes in Diseases of the Optic Nerve and Their Association with Visual Function / Shikha Chaganti, Jamie R. Robinson, Camilo Bermudez, Thomas Lasko, Louise A. Mawn, Bennett A. Landman
• Erratum to: Fast Predictive Simple Geodesic Regression / Zhipeng Ding, Greg Fleishman, Xiao Yang, Paul Thompson, Roland Kwitt, Marc Niethammer et al.

This book constitutes the refereed joint proceedings of the Third International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2017, and the 6th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2017, held in conjunction with the 20th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, in Quebec City, QC, Canada, in September 2017. The 38 full papers presented at DLMIA 2017 and the 5 full papers presented at ML-CDS 2017 were carefully reviewed and selected. The DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support.
(source: Nielsen Book Data)