<html><head><meta http-equiv="Content-Type" content="text/html charset=utf-8"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space;" class=""><div class="gmail-">DLMIA 2018 - 4th Workshop on Deep Learning in Medical Image Analysis (in conjunction with MICCAI 2018)</div><div class="gmail-"><br class="gmail-"></div><div class="gmail-">2nd Call for Papers</div><div class="gmail-"><br class="gmail-"></div><div class="gmail-">The workshop DLMIA has become one of the most successful MICCAI satellite events, with hundreds of attendees and more than 70 paper submissions in 2017. The 4th edition of DLMIA will be dedicated to the presentation of papers focused on the design and use of deep learning methods in medical image analysis applications. We believe that this workshop is setting the trends and identifying the challenges of the use of deep learning methods in medical image analysis. Another important objective of the workshop is to continue and increase the connection between software developers, researchers and end-users from diverse fields related to Medical Image and Signal Processing, which are the main scopes of MICCAI.</div><div class="gmail-"><br class="gmail-"></div><div class="gmail-">The proceedings will be published by SPRINGER under the “Lecture Notes in Computer Science” book series.</div><div class="gmail-"><br class="gmail-"></div><div class="gmail-">Topics:</div><div class="gmail-">- Medical imaging-based analysis using deep learning</div><div class="gmail-">- Medical signal-based analysis using deep learning</div><div class="gmail-">- Medical image reconstruction using deep learning</div><div class="gmail-">- Deep learning-oriented applications in medicine</div><div class="gmail-">- Image description and synthesis using deep learning techniques</div><div class="gmail-">- Deep learning model selection in medical imaging</div><div class="gmail-">- Multi-modal and multi-dimensional deep learning (3D, 4D, and beyond)</div><div class="gmail-">- Learning with noisy labels ( eg. crowdsourcing annotations, imperfect ground truth etc.)</div><div class="gmail-">- Integration of clinical variables with imaging data</div><div class="gmail-">- Deep learning for interventional image analysis</div><div class="gmail-">- Benchmarking and Evaluation of deep learning in clinical settings</div><div class="gmail-">- Active Deep Learning for medical imaging</div><div class="gmail-">- Reinforcement learning and Meta-learning in Medical Image Analysis</div><div class="gmail-"><br class="gmail-"></div><div class="gmail-">Important Dates:</div><div class="gmail-">- May 22nd : results released for MICCAI'18 papers</div><div class="gmail-">- June 11th (11:59pm PST) : DLMIA'18 paper submission deadline</div><div class="gmail-">- July 11th : DLMIA'18 paper notification of acceptance</div><div class="gmail-">- July 17th : DLMIA'18 Camera-ready version submission</div><div class="gmail-">- September 20th: Full day workshop</div><div class="gmail-"><br class="gmail-"></div><div class="gmail-">Invited Speakers:</div><div class="gmail-">Prof. Hayit Greenspan (Tel Aviv University)</div><div class="gmail-">Prof. Alison Noble (University of Oxford)</div><div class="gmail-">Mr. Christopher Semturs (Google Research) - Deep Learning for Retinal Imaging</div><div class="gmail-"><br class="gmail-"></div><div class="gmail-">Website:</div><div class="gmail-"><a href="http://cs.adelaide.edu.au/~dlmia4/" class="gmail-">http://cs.adelaide.edu.au/~dlmia4/</a></div><div class="gmail-"><br class="gmail-"></div><div class="gmail-">Organization:</div><div class="gmail-">Gustavo Carneiro, University of Adelaide, Australia</div><div class="gmail-">João Manuel R. S. Tavares, Universidade do Porto, Portugal</div><div class="gmail-">Andrew P. Bradley, University of Queensland, Australia</div><div class="gmail-">João Paulo Papa, Universidade Estadual Paulista, Brazil</div><div class="gmail-">Jacinto C. Nascimento, Instituto Superior Tecnico, Portugal</div><div class="gmail-">Vasileios Belagiannis, OSRAM, Germany</div><div class="gmail-">Zhi Lu, Guangdong University of Technology, China</div><div class=""><br class="webkit-block-placeholder"></div><div class="">
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