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<!DOCTYPE html>
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<title>Deep Learning for Human Brain Mapping</title>
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<h1 class="display-5">Deep Learning for Human Brain Mapping</h1>
<p class="lead">Deep learning has become an indispensable tool in computer vision, natural language processing, and is increasingly applied to neuroimaging data. It has very quickly surpassed human performance in natural image recognition and a variety of image-to-image translation methods are now popular as another tool to map the brain. In this annual course, we present state-of-the-art methods in deep learning with a focus on how these new techniques can readily be applied to your brain imaging data.</p>
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<h2>Deep Learning for Human Brain Mapping 2020</h2>
<h4>Organization for Human Brain Mapping. 13 July 2020.</h4>
<h5>Organizers: Andrew Doyle, Ariel Rokem</h5>
<li>Ariel Rokem - Hands-on Introduction to Deep Learning (<a href="https://colab.research.google.com/drive/1A9evLvSsdgwzGopK48q5MAM4sqQIqzSz" target="_blank">Notebook</a>, <a href="https://arokem.github.io/conv-nets-slides/#/" target="_blank">Slides</a>, <a href="https://www.pathlms.com/ohbm/courses/15243/sections/21757/video_presentations/159392" target="blank_">Video</a>)</li>
<li>Patrick McClure - Introduction to Bayesian Neural Networks for Neuroimaging (<a href="https://www.pathlms.com/ohbm/courses/15243/sections/21757/video_presentations/164041" target="blank_">Video</a>)</li>
<li>Anthony Bilodeau - Weakly supervised learning for quantitative analysis of biomedical images (<a href="https://colab.research.google.com/drive/1YLhzYRsMGn67EzFmSzKyMwXQaAMjxKfC" target="_blank">Notebook</a>, <a href="https://www.pathlms.com/ohbm/courses/15243/sections/21757/video_presentations/164038" target="blank_">Video</a>)</li>
<li>Amy Zhao - Making more data out of little data: Data augmentation for medical image segmentation (<a href="https://colab.research.google.com/drive/1SK6FBgc7LHEZKRpdkGDQXZYvgzn7BF3F", target="_blank">Notebook</a>, <a href="https://www.pathlms.com/ohbm/courses/15243/sections/21757/video_presentations/161122" target="blank_">Video</a>)</li>
<li>Robin Tibor Schirrmeister - Invertible Networks for EEG Decoding (<a href="https://colab.research.google.com/drive/1hKAFIHvdUafhfVxRVZRr3dZtU3IQaPLt" target="_blank">Notebook</a>, <a href="https://www.pathlms.com/ohbm/courses/15243/sections/21757/video_presentations/164035" target="blank_">Video</a>)</li>
<li>Yu Zhang - Graph Laplacian and Graph Neural Networks: A unified framework to analyze brain activity (<a href="https://github.com/zhangyu2ustc/gcn_tutorial_test" target="_blank">Notebook</a>, <a href="https://www.pathlms.com/ohbm/courses/15243/sections/21757/video_presentations/164037" target="blank_">Video</a>)</li>
<li>Adriana Romero - Attention mechanisms and their potential applications (<a href="https://www.pathlms.com/ohbm/courses/15243/sections/21757/video_presentations/164042" target="blank_">Video</a>)</li>
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<h2>Speakers</h2>
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<h5 class="card-title">Ariel Rokem (Organizer)</h5>
<h6 class="card-subtitle mb-2 text-muted">Senior Data Scientist, University of Washington </h6>
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<a href="https://scholar.google.ca/citations?user=hrBeLVYAAAAJ&hl=en&oi=ao" class="blk"><i class="ai ai-google-scholar-square ai-2x"></i></a>
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<p class="card-text">Ariel is a data scientist and hacker who runs the <a href="https://neurohackademy.org/" target="_blank">NeuroHackademy</a>, a bootcamp for neuroimagers to gain skills while doing code sprints on a variety of innovative projects pushing the limits of brain mapping. An instructor with Data/Software Carpentry since 2013, he's a strong believer in using <a href="https://www.pnas.org/content/115/36/8872.short" target="_blank">hackathons for education</a>, and is particularly interested in using structural MR imaging to map the brain.</p>
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<h5 class="card-title">Patrick McClure</h5>
<h6 class="card-subtitle mb-2 text-muted">Research Scientist, National Institute of Mental Health</h6>
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<a href="https://scholar.google.ca/citations?hl=en&user=8TKyUl4AAAAJ" class="blk" target="_blank"><i class="ai ai-google-scholar-square ai-2x"></i></a>
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<p class="card-text">Patrick uses deep learning combined with <a href="https://www.frontiersin.org/articles/10.3389/fninf.2019.00067/full" target="_blank">Baysian probabilistic modeling</a> to not only improve results in contexts of medical image segmentation, but to also get a measure of uncertainty in predictions. While an expert in computer vision and machine learning, he has also worked in <a href="https://www.biorxiv.org/content/10.1101/133504v2.abstract" target="_blank">computational neuroscience</a>, using deep learning networks to model the brain.</p>
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<h5 class="card-title">Anthony Bilodeau</h5>
<h6 class="card-subtitle mb-2 text-muted">PhD Candidate, Université de Laval</h6>
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<a href="https://scholar.google.com/citations?user=EE0x414AAAAJ" class="blk" target="_blank"><i class="ai ai-google-scholar-square ai-2x"></i></a>
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<p class="card-text">During his Master's in Biophotonics at <a href="https://cervo.ulaval.ca/en" target="_blank">CERVO</a> Anthony developed machine learning techniques to help biologists <a href="https://www.nature.com/articles/s41467-018-07668-y">investigate synaptic proteins at the nanoscale</a>. He's particularly enthusiastic about applying cutting-edge deep learning techniques to help researchers uncover the wonders of our brain.
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<h5 class="card-title">Amy Zhao</h5>
<h6 class="card-subtitle mb-2 text-muted">Research Scientist, Facebook Reality Labs</h6>
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<p class="card-text">Amy is a master of computer vision who specializes in <a href="https://arxiv.org/abs/1902.09383" target="_blank">realistic data augmentation</a>, transforming her input data to augment the space of realistic configurations they might appear in. She is a master of detecting <a href="https://www.cv-foundation.org/openaccess/content_iccv_2015_workshops/w18/html/Zhao_Estimating_a_Small_ICCV_2015_paper.html" target="_blank">weak signals in the presence of large noise sources</a>, and is at the forefront of generative modeling for learning robust models with limited data.</p>
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<h5 class="card-title">Robin Tibor Schirrmeister</h5>
<h6 class="card-subtitle mb-2 text-muted">PhD Candidate, University Freiburg</h6>
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<p class="card-text">In his early work Robin mapped the world, but he now maps the brain at the <a href="https://www.tnt.uni-freiburg.de/" target="_blank">Neuromedical AI Lab Freiburg</a>, with an emphasis on EEG. He is the lead developer of <a href="https://braindecode.github.io/" target="_blank">BrainDecode</a>, a software library for deep learning-based EEG decoding. He also has work on pure deep learning research, with a particular focus on <a href="https://arxiv.org/abs/1907.07746" target="_blank">invertible network architectures</a> that can potentially be used for more interpretable decoding than common approaches.</p>
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<h5 class="card-title">Yu Zhang</h5>
<h6 class="card-subtitle mb-2 text-muted">Postdoctoral Fellow, Université de Montréal</h6>
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<a href="https://scholar.google.ca/citations?user=lZwQ9mgAAAAJ&hl=en" class="blk" target="_blank"><i class="ai ai-google-scholar-square ai-2x"></i></a>
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Yu is a neuroscientist devoted to developing machine learning tools to better understand human brain organization. Her previous work involves building a new brain atlas using <a href="https://cdn.elifesciences.org/articles/26653/elife-26653-v2.pdf" target="_blank">diffusion</a> and <a href="https://www.sciencedirect.com/science/article/abs/pii/S1053811917301532" target="_blank">functional</a> MRI. Her current research interest is to decode and simulate brain activities using deep artificial neural networks, with a special focus on <a href="https://openreview.net/forum?id=HJenmmF8Ir" target="_blank">graph-based modeling</a>.</p>
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<h5 class="card-title">Adriana Romero</h5>
<h6 class="card-subtitle mb-2 text-muted">Research Scientist, FAIR<br>Adjunct Professor, McGill</h6>
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<p class="card-text">Adriana is on the forefront of machine learning research, and develops <a href="https://arxiv.org/abs/1710.10903" target="_blank">new methods</a> to make deep learning more closely model the structure of the data. From <a href="https://arxiv.org/abs/1611.09340" target="_blank">networks producing parameters for genetics analysis</a> to <a href="https://arxiv.org/abs/1902.03051" target="_blank">active acquisition for MRI reconstruction</a>, Adriana pushes the limits of what's possible in biomedical sciences.</p>
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<h2>Deep Learning for Human Brain Mapping 2019</h2>
<h4>Organization for Human Brain Mapping. Auditorium Parco della Musica, Rome.</h4>
<h5>9 June, 2019, 8:00 am - 5:00 pm</h5>
<ol>
<li>Deep Learning Approaches: Brief Introduction and Application to NeuroImaging - Vince Calhoun (<a href="https://www.pathlms.com/ohbm/courses/12238/sections/15846/video_presentations/139629" target="_blank">Video</a>)</li>
<li>Hands-on Introduction to Deep Learning with Keras - Ariel Rokem (<a href="https://colab.research.google.com/github/brainhack101/IntroDL/blob/master/notebooks/2019/Rokem/2019_Rokem_OHBM_IntroDL.ipynb" target="_blank">Notebook</a>, <a href="https://arokem.github.io/https://arokem.github.io/conv-nets-slides" target="_blank">Slides</a>, <a href="https://www.pathlms.com/ohbm/courses/12238/sections/15846/video_presentations/139608" target="_blank">Video</a>)</li>
<li>Revamped CNNs for Brain-Age Analyses - Saige Rutherford (<a href="https://colab.research.google.com/github/brainhack101/IntroDL/blob/master/notebooks/2019/Rutherford/CMI_T1w_GM_WM_CSFmasks_age_predict.ipynb" target="_blank">Notebook</a>, <a href="https://www.slideshare.net/SaigeRutherford/revamped-cnns-for-brains">Slides</a>, <a href="https://www.pathlms.com/ohbm/courses/12238/sections/15846/video_presentations/139630" target="_blank">Video</a>)</li>
<li>Segmenting the BigBrain - Hannah Spitzer (<a href="https://colab.research.google.com/github/brainhack101/IntroDL/blob/master/notebooks/2019/Spitzer/tutorial_segmentation.ipynb" target="_blank">Notebook</a>, <a href="https://github.com/brainhack101/IntroDL/raw/master/presentations/2019/2019-06_DL4HBM_segmentation.pdf">Slides</a>, <a href="https://www.pathlms.com/ohbm/courses/12238/sections/15846/video_presentations/139631" target="_blank">Video</a>)</li>
<li>Attention and Modeling of Cognitive Processes - Grace Lindsay (<a href="https://colab.research.google.com/github/brainhack101/IntroDL/blob/master/notebooks/2019/Lindsay/OHBMdemo.ipynb" target="_blank">Notebook</a>, <a href="https://github.com/brainhack101/IntroDL/raw/master/presentations/2019/ohbm_grace.pdf">Slides</a>, <a href="https://www.pathlms.com/ohbm/courses/12238/sections/15846/video_presentations/139670" target="_blank">Video</a>)</li>
<li>Synthesizing fMRI: Applications, Promises, and Pitfalls - Bliss Chapman (<a href="https://colab.research.google.com/github/brainhack101/IntroDL/blob/master/notebooks/2019/Chapman/ChapmanTutorial.ipynb" target="_blank">Notebook</a>, <a href="https://github.com/brainhack101/IntroDL/raw/master/presentations/2019/OHBM_2019_Brain_Gan_Tutorial.pdf" target="_blank">Slides</a>, <a href="https://www.pathlms.com/ohbm/courses/12238/sections/15846/video_presentations/139671" target="_blank">Video</a>)</li>
<li>GANs for Cross-Modality Image Synthesis - Anders Eklund (<a href="https://colab.research.google.com/github/brainhack101/IntroDL/blob/master/notebooks/2019/Eklund/CycleGAN.ipynb" target="_blank">Notebook</a>, <a href="https://github.com/brainhack101/IntroDL/raw/master/presentations/2019/Eklund_presentation.pdf" target="_blank">Slides</a>, <a href="https://www.pathlms.com/ohbm/courses/12238/sections/15846/video_presentations/139673" target="_blank">Video</a>)</li>
<li>Quantitative Susceptibility Mapping - Steffen Bollmann (<a href="https://colab.research.google.com/github/brainhack101/IntroDL/blob/master/notebooks/2019/Bollman/Steffen_Bollman_Deep_learning_QSM_tutorial_OHBM.ipynb" target="_blank">Notebook</a>, <a href="https://github.com/brainhack101/IntroDL/raw/master/presentations/2019/DeepQSM.pdf" target="_blank">Slides</a>, <a href="https://www.pathlms.com/ohbm/courses/12238/sections/15846/video_presentations/139672" target="_blank">Video</a>)</li>
<li>Segmenting Tracts in Diffusion MRI - Jakob Wasserthal (<a href="https://colab.research.google.com/github/brainhack101/IntroDL/blob/master/notebooks/2019/Wasserthal/TractSegTutorial.ipynb" target="_blank">Notebook</a>, <a href="https://github.com/brainhack101/IntroDL/raw/master/presentations/2019/slides_TractSeg.pdf" target="_blank">Slides</a>, <a href="https://www.pathlms.com/ohbm/courses/12238/sections/15846/video_presentations/139674" target="_blank">Video</a>)</li>
<li>Looking Inside the Black Box - Andrew Doyle (<a href="https://colab.research.google.com/drive/1EgdnWZeNqmzqEmnSR9PUnYXlTjeu1wAU" target="_blank">Notebook</a>, <a href="https://github.com/brainhack101/IntroDL/raw/master/presentations/2019/BlackBox.pdf" target="_blank">Slides</a>, <a href="https://www.pathlms.com/ohbm/courses/12238/sections/15846/video_presentations/139675" target="_blank">Video</a>)</li>
</ol>
</div>
</div>
<div class="row">
<div class="col-sm-10 col-md-10 col-lg-8 offset-sm-1 offset-md-1 offset-lg-2">
<hr>
<h2>Speakers</h2>
<br>
<div class="row">
<div class="col-sm-6">
<div class="card mb-3" style="height:95%">
<div class="card-body">
<h5 class="card-title">Vince Calhoun</h5>
<h6 class="card-subtitle mb-2 text-muted">Distinguished Professor, University of New Mexico</h6>
<div class="row">
<div class="col-sm-4 text-center">
<img src="img/vince_calhoun.jpg" width="150px" class="img-fluid rounded-circle mt-3"/>
<p class="card-text">
<br>
<a href="https://scholar.google.ca/citations?user=7pk1M78AAAAJ&hl=en" class="blk"><i class="ai ai-google-scholar-square ai-2x"></i></a>
<a href="https://twitter.com/vdcalhoun" class="blk"><i class="fab fa-twitter fa-2x"></i></a>
<br>
</p>
</div>
<div class="col-sm-8">
<p class="card-text">Vince was one of the first scientists to get deep learning to work at all in neuroimaging, and has applied it extensively to modeling functional magnetic resonance imaging to build better maps of the brain. His group has investigated comparatively rare models like <a href="https://www.frontiersin.org/articles/10.3389/fnins.2014.00229/full" target="_blank">deep Boltzmann machines</a> and has investigated using deep learning for <a href="https://www.sciencedirect.com/science/article/pii/S2451902216000598" target="_blank">fusing modalities at multiple brain scales</a>.
</p>
</div>
</div>
</div>
</div>
</div>
<div class="col-sm-6">
<div class="card mb-3" style="height:95%">
<div class="card-body">
<h5 class="card-title">Ariel Rokem (Organizer)</h5>
<h6 class="card-subtitle mb-2 text-muted">Senior Data Scientist, University of Washington </h6>
<div class="row">
<div class="col-sm-4 text-center">
<img src="img/ariel_rokem.png" width="150px" class="img-fluid rounded-circle mt-3"/>
<p class="card-text">
<br>
<a href="https://scholar.google.ca/citations?user=hrBeLVYAAAAJ&hl=en&oi=ao" class="blk"><i class="ai ai-google-scholar-square ai-2x"></i></a>
<a href="https://twitter.com/arokem" class="blk"><i class="fab fa-twitter fa-2x"></i></a>
<br>
</p>
</div>
<div class="col-sm-8">
<p class="card-text">Ariel is a data scientist and hacker who runs the <a href="https://neurohackademy.org/" target="_blank">NeuroHackademy</a>, a bootcamp for neuroimagers to gain skills while doing code sprints on a variety of innovative projects pushing the limits of brain mapping. An instructor with Data/Software Carpentry since 2013, he's a strong believer in using <a href="https://www.pnas.org/content/115/36/8872.short" target="_blank">hackathons for education</a>, and is particularly interested in using structural MR imaging to map the brain.</p>
</div>
</div>
</div>
</div>
</div>
</div>
<div class="row">
<div class="col-sm-6">
<div class="card mb-3" style="height:95%">
<div class="card-body">
<h5 class="card-title">Saige Rutherford</h5>
<h6 class="card-subtitle mb-2 text-muted">Research Staff, University of Michigan</h6>
<div class="row">
<div class="col-sm-4 text-center">
<img src="img/saige_rutherford.jpg" width="150px" class="img-fluid rounded-circle mt-3"/>
<p class="card-text">
<br>
<a href="https://scholar.google.ca/citations?user=ryF481YAAAAJ&hl=en" class="blk"><i class="ai ai-google-scholar-square ai-2x"></i></a>
<a href="https://twitter.com/being_saige" class="blk"><i class="fab fa-twitter fa-2x"></i></a></p>
<br>
</div>
<div class="col-sm-8">
<p class="card-text">Saige studies neuro-development, and is interested in discovering <a href="https://www.biorxiv.org/content/early/2018/06/29/326082" target="_blank">low-dimensional representations</a> that might predict how we grow. She uses deep learning and careful MR sequence design to characterize neo-natal brain growth in challenging segmentation environments and <a href="https://arxiv.org/abs/1808.04362" target="_blank">predict brain age</a> as a biomarker for early detection of disorders and degeneration.</p>
</div>
</div>
</div>
</div>
</div>
<div class="col-sm-6">
<div class="card mb-3" style="height:95%">
<div class="card-body">
<h5 class="card-title">Hannah Spitzer</h5>
<h6 class="card-subtitle mb-2 text-muted">Doctoral Candidate, Forschungszentrum Jülich</h6>
<div class="row">
<div class="col-sm-4 text-center">
<img src="img/hannah_spitzer.jpg" width="150px" class="img-fluid rounded-circle mt-3"/>
<p class="card-text">
<br>
<a href="https://scholar.google.ca/citations?user=MK5WcD8AAAAJ&hl=en" class="blk">
<i class="ai ai-google-scholar-square ai-2x"></i></a>
<a href="https://twitter.com/HannahSpitzer1" class="blk"><i class="fab fa-twitter fa-2x"></i></a>
</p>
</div>
<div class="col-sm-8">
<p class="card-text">Hannah is a computer vision expert who uses big data to <a href="https://ieeexplore.ieee.org/abstract/document/7950666">map the brain at the microscopic level</a>. She is interested in how deep learning can help us characterize and discover the variability in the <a href="https://arxiv.org/abs/1806.05104" target="_blank">cytoarchitecture</a> of our brains, and works on understanding the connectivity and laminar structure of the reconstructed histological data in the <a href="https://bigbrain.loris.ca/main.php" target="_blank">BigBrain</a>.</p>
</div>
</div>
</div>
</div>
</div>
</div>
<div class="row">
<div class="col-sm-6">
<div class="card mb-3" style="height:95%">
<div class="card-body">
<h5 class="card-title">Grace Lindsay</h5>
<h6 class="card-subtitle mb-2 text-muted">Postdoctoral Fellow, Columbia University</h6>
<div class="row">
<div class="col-sm-4 text-center">
<img src="img/grace_lindsay.jpg" width="150px" class="img-fluid rounded-circle mt-3"/>
<p class="card-text">
<br>
<a href="https://scholar.google.ca/citations?user=4kETHY4AAAAJ&hl=en" class="blk"><i class="ai ai-google-scholar-square ai-2x"></i></a>
<a href="https://twitter.com/neurograce" class="blk"><i class="fab fa-twitter fa-2x"></i></a>
<br>
</p>
</div>
<div class="col-sm-8">
<p class="card-text">Grace is interested in parallels between how biological neural circuits relate to artificial models. Using deep learning tools, she studies <a href="https://www.youtube.com/watch?v=viderYmdiH8" target="_blank">vision and attention</a>, with a particular interest in how <a href="http://www.jneurosci.org/content/early/2017/10/11/jneurosci.1222-17.2017" target="_blank">Hebbian learning</a> in artificial networks is reflected in brain recording data. She is particularly strong at scientific communication, and her <a href="http://unsupervisedthinkingpodcast.blogspot.com/" target="_blank">podcast</a> offers listeners a gentle and accessible introduction to all things neuro.
</p>
</div>
</div>
</div>
</div>
</div>
<div class="col-sm-6">
<div class="card mb-3" style="height:95%">
<div class="card-body">
<h5 class="card-title">Bliss Chapman</h5>
<h6 class="card-subtitle mb-2 text-muted">Research Staff, University of Illinois</h6>
<div class="row">
<div class="col-sm-4 text-center">
<img src="img/bliss_chapman.jpg" width="150px" class="img-fluid rounded-circle mt-3"/>
<p class="card-text">
<br>
<a href="https://twitter.com/chapman_bliss" class="blk"><i class="fab fa-twitter fa-2x"></i></a>
<br>
</p>
</div>
<div class="col-sm-8">
<p class="card-text">Bliss is a computer scientist, statistician, and trained classical ballet dancer. He explores how deep generative models of biomedical data can be used to quantify variability between populations and improve statistical power analyses. He has also explored how machine learning techniques can be applied to characterize low dimensional connectome dynamics.</p>
</div>
</div>
</div>
</div>
</div>
<div class="col-sm-6">
<div class="card mb-3" style="height:95%">
<div class="card-body">
<h5 class="card-title">Anders Eklund</h5>
<h6 class="card-subtitle mb-2 text-muted">Associate Professor, Linköping University</h6>
<div class="row">
<div class="col-sm-4 text-center">
<img src="img/anders_eklund.jpg" width="150px" class="img-fluid rounded-circle mt-3"/>
<p class="card-text">
<br>
<a href="https://scholar.google.ca/citations?user=x6pIxSEAAAAJ&hl=en" class="blk"><i class="ai ai-google-scholar-square ai-2x"></i></a>
<a href="https://twitter.com/wandedob" class="blk"><i class="fab fa-twitter fa-2x"></i></a>
<br>
</p>
</div>
<div class="col-sm-8">
<p class="card-text">Anders is a computational guru whose extensive work <a href="https://github.com/wanderine/BROCCOLI" target="_blank">parallelizing neuroimaging pipelines on GPUs</a> made deep learning a natural extension of his research program. He's interested in <a href="https://arxiv.org/abs/1806.07777" target="_blank">pushing the limits of generative neural network models</a> to fill in missing modalities and scale large-scale neuroimaging research across incomplete datasets.
</p>
</div>
</div>
</div>
</div>
</div>
<div class="col-sm-6">
<div class="card mb-3" style="height:95%">
<div class="card-body">
<h5 class="card-title">Steffen Bollmann</h5>
<h6 class="card-subtitle mb-2 text-muted">Research Fellow, University of Queensland</h6>
<div class="row">
<div class="col-sm-4 text-center">
<img src="img/steffen_bollman.jpg" width="150px" class="img-fluid rounded-circle mt-3"/>
<p class="card-text">
<br>
<a href="https://scholar.google.ca/citations?user=HmXlj24AAAAJ&hl=en" class="blk"><i class="ai ai-google-scholar-square ai-2x"></i></a>
<a href="https://twitter.com/sbollmann_MRI" class="blk"><i class="fab fa-twitter fa-2x"></i></a>
<br>
</p>
</div>
<div class="col-sm-8">
<p class="card-text">
Steffen records high-resolution magnetic resonance imaging to create quantitative susceptibility maps that reflect information on biological tissue properties, predominantly myelin, iron and calcium. He uses deep learning to <a href="https://www.biorxiv.org/content/early/2018/03/07/278036" target="_blank">estimate these maps</a> to study neurodegenerative diseases.
</p>
</div>
</div>
</div>
</div>
</div>
</div>
<div class="row">
<div class="col-sm-6">
<div class="card mb-3" style="height:95%">
<div class="card-body">
<h5 class="card-title">Jakob Wasserthal</h5>
<h6 class="card-subtitle mb-2 text-muted">Doctoral Candidate, DKFZ</h6>
<div class="row">
<div class="col-sm-4 text-center">
<img src="img/jakob_wasserthal.jpg" width="150px" class="img-fluid rounded-circle mt-3"/>
<p class="card-text">
<br>
<a href="https://twitter.com/JakobWasserthal" class="blk"><i class="fab fa-twitter fa-2x"></i></a>
<br>
</p>
</div>
<div class="col-sm-8">
<p class="card-text">Jakob has innovatively applied standard deep learning segmentation techniques hierarchically to <a href="https://github.com/MIC-DKFZ/TractSeg" target="_blank">produce maps of individual tracts in diffusion MRI</a>. He is extending this so that it <a href="https://arxiv.org/pdf/1809.10486v1.pdf" target="_blank">easily generalizes to other problems</a> and these deep architectures are more accessible for researchers to be able to adapt them to their specific use cases.</p>
</div>
</div>
</div>
</div>
</div>
<div class="col-sm-6">
<div class="card mb-3" style="height:95%">
<div class="card-body">
<h5 class="card-title">Andrew Doyle (Organizer)</h5>
<h6 class="card-subtitle mb-2 text-muted">Research Staff, McGill University</h6>
<div class="row">
<div class="col-sm-4 text-center">
<img src="img/andrew_doyle2.jpg" width="150px" class="img-fluid rounded-circle mt-3"/>
<p class="card-text">
<br>
<a href="https://scholar.google.ca/citations?user=xxecIhQAAAAJ&hl=en" class="blk"><i class="ai ai-google-scholar-square ai-2x"></i></a>
<a href="https://twitter.com/crocodoyle" class="blk"><i class="fab fa-twitter fa-2x"></i></a>
<br>
</p>
</div>
<div class="col-sm-8">
<p class="card-text">Andrew teaches neuroimagers the basics (and advanced) of deep learning methods in order to recruit collaborators who will teach him some neuroscience. This year, he co-organized hands-on educational courses at <a href="http://restingstate.com/2018/satellite-symposia/" target="_blank">Resting State and Brain Connectivity</a>, the <a href="https://brainhack101.github.io/introML/dl-course-outline.html" target="_blank">Montreal Artificial Intelligence and Neuroscience conference</a>, and introduced deep learning as an instructor at the <a href="https://brainhackmtl.github.io/school2018/index" target="_blank">BrainHack Summer School</a>.
</p>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
<div class="row">
<div class="col-sm-10 col-md-10 col-lg-8 offset-sm-1 offset-md-1 offset-lg-2">
<hr>
<h2>Beyond Linear Decoding: Introduction to Deep Learning Methods</h2>
<h4>Organization for Human Brain Mapping 2018. Suntec Convention Center, Singapore.</h4>
<h5>17 June 2018, 1:00 pm - 5:00 pm</h4>
<ol>
<li>Beyond Linear Decoding - Pamela K. Douglas</li>
<ul><li><a href="https://www.pathlms.com/ohbm/courses/8246/sections/12542/video_presentations/115836" target="_blank">video (23 mins)</a></li></ul>
<li>Intro to Deep Learning - Andrew Doyle
<ul><li><a href="https://www.pathlms.com/ohbm/courses/8246/sections/12542/video_presentations/115827" target="_blank">video (28 mins)</a></li></ul>
<ul><li><a href="https://www.slideshare.net/AndrewDoyle12/introduction-to-deep-learning-for-neuroimaging" target="_blank">slides</a></li></ul></li>
<li>Hands-on Deep Learning with Keras - Anisha Keshavan
<ul><li><a href="https://www.pathlms.com/ohbm/courses/8246/sections/12542/video_presentations/115840" target="_blank">video (25 mins)</a></li></ul>
<ul><li><a href="http://nbviewer.jupyter.org/github/brainhack101/IntroDL/blob/master/IntroToKeras.ipynb" target="_blank">Jupyter Notebook</a></li></ul></li>
<li>Generative Adversarial Networks - Christopher Beckham
<ul>
<li><a href="https://www.pathlms.com/ohbm/courses/8246/sections/12542/video_presentations/115839" target="_blank">video (27 mins)</a></li>
<li><a href="https://github.com/brainhack101/IntroDL/blob/master/presentations/ChristopherBeckhamGANs.pdf" target="_blank">slides</a></li>
<li><a href="http://nbviewer.jupyter.org/github/brainhack101/IntroDL/blob/master/LSGAN_MLP_in_Keras.ipynb" target="_blank">GAN tutorial</a></li>
</ul>
</li>
<li>Deep Learning for Segmenting Infant MRI - Pim Moeskops
<ul>
<li><a href="https://www.pathlms.com/ohbm/courses/8246/sections/12542/video_presentations/115838" target="_blank">video (26 mins)</a></li>
<li><a href="https://github.com/brainhack101/IntroDL/blob/master/presentations/PimMoeskopsSegmentation.pdf" target="_blank">slides</a></li>
<li><a href="http://nbviewer.jupyter.org/github/tueimage/8DM20-deeplearning-keras/blob/master/mnist_practical_keras.ipynb" target="_blank">computer vision course tutorial notebook</a></li>
</ul>
</li>
<li>Explaining Decisions of Neural Networks and Layer-wise Relevance Propagation - Alexander Binder
<ul>
<li><a href="https://www.pathlms.com/ohbm/courses/8246/sections/12542/video_presentations/115841" target="_blank">video (39 mins)</a></li>
<li><a href="https://www.dropbox.com/s/6x1mc1o3b7ikis7/talk_ohbm_2018.pdf?dl=0" target="_blank">slides</a></li>
<li><a href="https://lrpserver.hhi.fraunhofer.de/" target="_blank">LRP demo</a></li>
<li><a href="http://www.heatmapping.org/tutorial/" target="_blank">LRP tutorial</a></li>
<li><a href="https://github.com/sebastian-lapuschkin/lrp_toolbox" target="_blank">Keras interpretability toolbox</a></li>
</ul>
</li>
</ol>
</div>
<div class="row">
<div class="col-sm-10 col-md-10 col-lg-8 offset-sm-1 offset-md-1 offset-lg-2">
<hr>
<h2>Speakers</h2>
<br>
<div class="row">
<div class="col-sm-6">
<div class="card mb-3" style="height:95%">
<div class="card-body">
<h5 class="card-title">Pamela K. Douglas (Organizer)</h5>
<h6 class="card-subtitle mb-2 text-muted">Assistant Professor, University of California (Los Angeles)</h6>
<h6 class="card-subtitle mb-2 text-muted">Assistant Professor, University of Central Florida</h6>
<div class="row">
<div class="col-sm-4 text-center">
<img src="img/Pamela_Douglas.jpg" width="150px" class="img-fluid rounded-circle mt-3"/>
<p class="card-text">
<br>
<a href="https://scholar.google.ca/citations?user=w0UrHXYAAAAJ&hl=en&oi=sra" class="blk"><i class="ai ai-google-scholar-square ai-2x"></i></a>
<a href="https://twitter.com/pamelitadouglas" class="blk"><i class="fab fa-twitter fa-2x"></i></a>
<br>
</p>
</div>
<div class="col-sm-8">
<p class="card-text">Pamela uses machine learning to study Attention Deficit and Hyperactivity Disorder in fMRI and EEG data. An expert in unsupervised learning, she also recently presented work on the <a href="http://www.interpretable-ml.org/nips2017workshop/papers/15.pdf">interpretability of fMRI weight maps</a> at the Interpreting, Explaining and Visualizing Deep Learning Workshop of NIPS 2017, and co-organized the <a href="http://niml2017.org/">Neuroimaging and Machine Learning 2017 Workshop</a>.</p>
</div>
</div>
</div>
</div>
</div>
<div class="col-sm-6">
<div class="card mb-3" style="height:95%">
<div class="card-body">
<h5 class="card-title">Andrew Doyle (Organizer)</h5>
<h6 class="card-subtitle mb-2 text-muted">Research Software Developer, McGill Centre for Integrative Neuroscience</h6>
<div class="row">
<div class="col-sm-4 text-center">
<img src="img/Andrew_Doyle.jpg" width="150px" class="img-fluid rounded-circle mt-3""/>
<p class="card-text">
<br>
<a href="https://scholar.google.ca/citations?user=xxecIhQAAAAJ&hl=en" class="blk"><i class="ai ai-google-scholar-square ai-2x"></i></a>
<a href="https://twitter.com/crocodoyle" class="blk"><i class="fab fa-twitter fa-2x"></i></a></p>
<br>
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<p class="card-text">Andrew uses deep learning to automate portions of neuroimaging workflows. His work automating infant structural MRI quality control is being integrated into the <a href="http://loris.ca/">LORIS</a> databasing system, and he now works on using deep learning to predict future diagnoses in Alzheimer's Disease, Autism Spectrum Disorder, and Major Depression. He recently organized a <a href="https://brainhack101.github.io/deepbrainhack2017/">deep learning-themed Brainhack</a>.</p>
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<h5 class="card-title">Anisha Keshavan</h5>
<h6 class="card-subtitle mb-2 text-muted">Postdoctoral Fellow, University of Washington</h6>
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<p class="card-text">Anisha combines the speed of inference in deep learning and crowdsourcing techniques to achieve expert-level ratings for structural MRI scans. Co-organizer of this year's <a href="https://ohbm.github.io/hackathon2018/">OHBM hackathon</a>, Anisha develops open-source web apps like <a href="https://braindr.us">Braindr</a> and <a href="http://mindcontrol-hbn.herokuapp.com/">Mind Control</a> to facilitate the analysis of neuroimaging data - and construct great datasets for deep learning.</p>
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<h5 class="card-title">Christopher Beckham</h5>
<h6 class="card-subtitle mb-2 text-muted">Graduate Student, Montreal Institute for Learning Algorithms</h6>
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<p class="card-text">Chris has made contributions to the <a href="https://www.cs.waikato.ac.nz/ml/">WEKA</a> toolbox at the University of Waikato in New Zealand, which hosts one of the largest open-source repositories of learning algorithms in the world. He now uses Generative Adversarial Networks (GANs) to bring the power of deep learning to smaller-sized medical imaging datasets.</p>
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<h5 class="card-title">Pim Moseskops</h5>
<h6 class="card-subtitle mb-2 text-muted">Postdoctoral Fellow, TU Eindhoven</h6>
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<p class="card-text">Pim uses cutting-edge deep learning techniques to characterize neurodevelopment, and has worked on (preterm) infants and adult brain MRI. His work goes beyond automatic tissue segmentation to allow quantification of brain characteristics and prediction of neurodevelopmental impairments. He recently co-organised MICCAI workshops on neonatal, fetal and pediatric image analysis (<a href="http://pippi.cs.ucl.ac.uk/pippi2016/">PIPPI2016</a>, <a href="http://dblp1.uni-trier.de/db/conf/miccai/fifi2017.html">FIFI2017</a>) and is organizing the IEEE ISBI special session on <a href="http://biomedicalimaging.org/2018/special-sessions/image-analysis-of-the-developing-brain/">Image Analysis of the Developing Brain</a> in April 2018.
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<h5 class="card-title">Alexander Binder</h5>
<h6 class="card-subtitle mb-2 text-muted">Assistant Professor, Singapore University of Technology and Design</h6>
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<p class="card-text">Alex is an expert in computer vision and interpretable machine learning. He has moved on from designing self-driving cars' localization systems and object recognition systems to medical applications. Co-organizer of the ACCV 2016 <a href="http://www.interpretable-ml.org/accv2016workshop/">Workshop on Interpretation and Visualization of Deep Neural Nets</a>, he has some of the best insight into the interpretability of deep neural networks in the world, winning the Best Paper award at ICML 2016 with his paper <a href="http://iphome.hhi.de/samek/pdf/BinICML16.pdf">Analyzing and Validating Neural Networks Predictions</a>.
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<h2>Learning Resources</h2>
<p>Non-exhaustive list of learning materials for those who want more!</p>
<ul>
<li><a href="https://www.deeplearningbook.org/">Deep Learning Textbook</a> by Ian Goodfellow, Aaron Courville and Yoshua Bengio</li>
<li>Hugo Larochelle's <a href="https://www.youtube.com/watch?v=SGZ6BttHMPw&list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH">course on Deep Learning</a></li>
<li><a href="http://deeplearning.net/">deeplearning.net</a> links to many resources</li>
<li><a href="http://gael-varoquaux.info/interpreting_ml_tuto/" target="_blank">Interpreting machine learning models</a> tutorial by Gael Varoquaux</li>
</ul>
<h2>Development Resources</h2>
<p>Deep learning typically involves running the backpropagation algorithm on a large computational graph. This can be parallelized efficiently and easily with the help of tensor math libraries like those listed below. We recommend using Python 3 for this, but <a href="https://www.mathworks.com/discovery/deep-learning.html">MATLAB now has deep learning built-in</a> if you just can't quit closed-source software.</p>
<ul>
<li>Google <a href="https://colab.research.google.com">Colaboratory</a> is Google Docs for <a href="http://jupyter.org/">Jupyter Notebooks</a>, and allows you train models with GPUs for free!</li>
<li>Install <a href="https://conda.io/docs/user-guide/install/download.html">Anaconda</a>, recommended way to get Python (especially for Windows users)</li>
<li><a href="https://keras.io">Keras</a>, a high-level neural network API that runs on top of <a href="https://www.tensorflow.org/">TensorFlow</a>, <a href="https://www.microsoft.com/en-us/cognitive-toolkit/">CNTK</a> or <a href="http://deeplearning.net/software/theano/">Theano</a></li>
<li>Install <a href="https://www.python.org/">Python</a> (recommended if Anaconda doesn't work)</li>
<li>For GPU training, install <a href="https://developer.nvidia.com/cuda-toolkit">CUDA</a> and <a href="https://developer.nvidia.com/cudnn">cuDNN</a></li>
<li><a href="https://docs.docker.com/install/">Docker</a> users can build deep learning containers on top of the <a href="https://github.com/NVIDIA/nvidia-docker">Docker Engine Utility for NVIDIA GPUs</a>. Note that you still need CUDA/cuDNN</li>
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<p class="text-center"> © Team Beyond Linear 2018</p>
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