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Scope
'Medical Imaging meets NIPS' is a satellite workshop at NIPS 2017. The workshop aims to bring researchers together from the medical image computing and machine learning community. The objective is to discuss the major challenges in the field and opportunities for joining forces. The event will feature a series of high-profile invited speakers from industry, academia, engineering and medical sciences who aim to give an overview of recent advances, challenges, latest technology and efforts for sharing clinical data.
Motivation
Medical imaging is facing a major crisis with an ever increasing complexity and volume of data and immense economic pressure. The interpretation of medical images pushes human abilities to the limit with the risk that critical patterns of disease go undetected. Machine learning has emerged as a key technology for developing novel tools in computer aided diagnosis, therapy and intervention. Still, progress is slow compared to other fields of visual recognition which is mainly due to the domain complexity and constraints in clinical applications which require most robust, accurate, and reliable solutions.
Call for Abstracts
We invite submissions of extended abstracts for poster presentation during the workshop. Submitting an abstract is an ideal way of engaging with the workshop and to showcase research in the area of machine learning for medical imaging. Submitted work does not have to be original and can be already published elsewhere and/or can be of preliminary nature. There will be no workshop proceedings, and the poster session may be conditional on receiving sufficiently many submissions. Accepted abstracts together with author information will be made available on this website.
Dates
Submissions: Sunday, October 29th, midnight PST
Notifications: Sunday, November 5th
Workshop: Saturday, December 9th, 8:45 AM - 6 PM
Schedule (tentative)
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Sat 8:45 a.m. - 9:00 a.m.
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Opening
(
Introductory
)
Opening remarks by the organizers |
Ben Glocker · Ender Konukoglu · Hervé Lombaert · Kanwal Bhatia 🔗 |
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Sat 9:00 a.m. - 9:30 a.m.
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Shaping the future through innovations: Artificial intelligence for healthcare (Siemens)
(
Talk
)
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Dorin Comaniciu 🔗 |
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Sat 9:30 a.m. - 10:00 a.m.
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Role of AI and Deep Learning in Radiology (IBM)
(
Talk
)
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Tanveer Syeda-Mahmood 🔗 |
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Sat 10:00 a.m. - 10:30 a.m.
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Pixel Perfectionism - Machine learning and Adaptive Radiation Therapy (University of Cambridge & Microsoft Research)
(
Talk
)
|
Rajesh Jena 🔗 |
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Sat 10:30 a.m. - 11:00 a.m.
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Coffee break - Morning
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🔗 |
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Sat 10:30 a.m. - 11:00 a.m.
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Poster session - Morning
(
Poster Session
)
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Yongchan Kwon · Young-geun Kim · Ender Konukoglu · Peter Li · John Guibas · Tejpal Virdi · Kuldeep Kumar · Morteza Mardani · Jelmer Wolterink · Enhao Gong · Natalia Antropova · Johannes Stelzer · Rene Bidart · Wei-Hung Weng · Martin Rajchl · Marc Górriz · Vineeta Singh · Christopher Sandino · Hiba Chougrad · Bob Hu · Isaac Godfried · Ke Xiao · Heliodoro Tejeda Lemus · Jordan Harrod · ILSANG WOO · Vincent Chen · Joseph Cheng · Vikash Gupta · Chuck-Hou Yee · Ben Glocker · Hervé Lombaert · Maximilian Ilse · Aneta Lisowska · Andrew Doyle · Milad Mckie
|
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Sat 11:00 a.m. - 11:30 a.m.
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Deep learning for cardiovascular image analysis (University Medical Center Utrecht)
(
Talk
)
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Ivana Isgum 🔗 |
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Sat 11:30 a.m. - 12:00 p.m.
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Deep learning for image reconstruction, segmentation and super-resolution in medical imaging (Imperial College London)
(
Talk
)
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Daniel Rueckert 🔗 |
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Sat 12:00 p.m. - 12:30 p.m.
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Biomedical Imaging and Genetic (BIG) Data Analytics in Dementia and Oncology (Erasmus MC Rotterdam, Quantib)
(
Talk
)
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Wiro Niessen 🔗 |
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Sat 12:30 p.m. - 1:30 p.m.
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Lunch break
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🔗 |
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Sat 1:30 p.m. - 2:00 p.m.
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Scikit-learn & nilearn: Democratisation of machine learning for brain imaging (INRIA)
(
Talk
)
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Gael Varoquaux 🔗 |
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Sat 2:00 p.m. - 2:30 p.m.
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The Multimodal Brain Tumor Segmentation Challenge (TU Munich)
(
Talk
)
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Bjoern Menze 🔗 |
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Sat 2:30 p.m. - 3:00 p.m.
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DeepLumen: Accurate Vessel Segmentation for Better Cardiovascular Care (HeartFlow)
(
Talk
)
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Kersten Petersen 🔗 |
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Sat 3:00 p.m. - 3:30 p.m.
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Coffee break - Afternoon
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🔗 |
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Sat 3:00 p.m. - 3:30 p.m.
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Poster session - Afternoon
(
Poster Session
)
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Yongchan Kwon · Young-geun Kim · Ender Konukoglu · Peter Li · John Guibas · Tejpal Virdi · Kuldeep Kumar · Morteza Mardani · Jelmer Wolterink · Enhao Gong · Natalia Antropova · Johannes Stelzer · Rene Bidart · Wei-Hung Weng · Martin Rajchl · Marc Górriz · Vineeta Singh · Christopher Sandino · Hiba Chougrad · Bob Hu · Isaac Godfried · Ke Xiao · Heliodoro Tejeda Lemus · Jordan Harrod · ILSANG WOO · Vincent Chen · Joseph Cheng · Vikash Gupta · Chuck-Hou Yee · Ben Glocker · Hervé Lombaert · Maximilian Ilse · Aneta Lisowska · Andrew Doyle · Milad Mckie
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Sat 3:30 p.m. - 4:00 p.m.
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NiftyNet: An open-source comunity-driven framework for neural networks in medical imaging (University College London)
(
Talk
)
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M. Jorge Cardoso 🔗 |
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Sat 4:00 p.m. - 4:30 p.m.
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Winner of The Digital Mammography DREAM Challenge (Therapixel)
(
Talk
)
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Yaroslav Nikulin 🔗 |
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Sat 4:30 p.m. - 5:15 p.m.
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Panel discussion
(
Panel Discussion
)
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🔗 |
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Sat 5:00 p.m. - 5:15 p.m.
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Closing
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Ben Glocker · Ender Konukoglu · Hervé Lombaert · Kanwal Bhatia 🔗 |
Author Information
Ben Glocker (Imperial College London)
Ender Konukoglu (ETH Zurich)
Hervé Lombaert (ETS Montreal / Inria)
Hervé is Associate Professor at ETS Montreal, Canada and Affiliated Research Scientist at Inria, France - His research interests are in Statistics on Shapes, Data & Medical Images. He had the chance to work in multiple centers, including Microsoft Research (Cambridge, UK), Siemens Corporate Research (Princeton, NJ), Inria Sophia-Antipolis (France), McGill University (Canada), and Polytechnique Montreal (Canada). He is also a recipient of the François Erbsmann Prize, a top prize in Medical Image Analysis, earned a Best Thesis Award at Polytechnique Montreal, as well as several other prizes and fellowships - Hervé co-organized several workshops and special sessions in major international conferences, including NIPS and ICML, on Medical Image Analysis.
Kanwal Bhatia (Visulytix)
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