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<!doctype html>
<html lang="en">
<head>
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<title>ICASSP 2023</title>
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<!-- In between the <div="reveal"> and the <div class="slides">-->
<!-- Any section element inside of this container is displayed as a slide -->
<div class="slides">
<section>
<section>
<p>
<h2>GLACIER: glass-box transformer</h2>
<h3>for interpretable dynamic neuroimaging</h3>
<h4><h4>
<p> <alert>Usman Mahmood</alert>, Zening Fu, Vince Calhoun, Sergey Plis
<div>
<img style="bottom=0; width=100%; box-shadow: 0px 0px 0px
rgba(150, 150, 255,0.8);
border:0;"
src="figures/IJCNN2022talk_team.svg"
alt="IJCNN team">
</div>
<p>
<img style="border:0; box-shadow: 0px 0px 0px
rgba(150, 150, 255,
0.8);" width="150"
src="figures/ICASSP2023.png"
alt="IJCNN">
<img style="border:0;
box-shadow: 0px
0px 0px;"
width="300"
src="figures/TReNDS_logo_light.png"
alt="logo">
<img style="border:0; box-shadow: 0px 0px 0px
rgba(150, 150, 255,
0.8);" height="100"
src="figures/neuroneural_logo.png"
alt="Neuroneural">
</section>
<section>
<h3>Multivariate Time Series</h3>
<row>
<col50>
<ul>
<li class="fragment roll-in" data-fragment-index="0"> Electrophysiological
<li class="fragment roll-in" data-fragment-index="1"> Geospatial
<li class="fragment roll-in" data-fragment-index="2"> Microphone arrays
<li class="fragment roll-in" data-fragment-index="3"> Internet of things
<li class="fragment roll-in" data-fragment-index="4"> Distributed sensing
<li class="fragment roll-in" data-fragment-index="5"> Health monitoring
</ul>
</col50>
<col>
<div style="position:relative; width:480px; height:480px; margin:auto;">
<img class="fragment current-visible" data-transition="slide fade-out" data-fragment-index="0" width="640" height="480" src="figures/EEG_sleep_t.png" style="position:absolute;top:0;left:0;border:0; box-shadow: 0px 0px 0px rgba(255, 255, 255, 255);" />
<img class="fragment current-visible" data-transition="slide fade-out" data-fragment-index="1" width="640" height="480" src="figures/Seismic_comparison.png" style="position:absolute;top:0;left:0;border:0; box-shadow: 0px 0px 0px rgba(255, 255, 255, 255);" />
<img class="fragment current-visible" data-transition="slide fade-out" data-fragment-index="2" width="640" height="480" src="figures/Matrix_microphones.png" style="position:absolute;top:0;left:0;border:0; box-shadow: 0px 0px 0px rgba(255, 255, 255, 255);" />
<img class="fragment current-visible" data-transition="slide fade-out" data-fragment-index="3" width="640" height="480" src="figures/smart_city_sq.png" style="position:absolute;top:0;left:0;border:0; box-shadow: 0px 0px 0px rgba(255, 255, 255, 255);" />
<img class="fragment current-visible" data-transition="slide fade-out" data-fragment-index="4" width="640" height="480" src="figures/distributed_sensing.png" style="position:absolute;top:0;left:0;border:0; box-shadow: 0px 0px 0px rgba(255, 255, 255, 255);" />
<img class="fragment current-visible" data-transition="slide fade-out" data-fragment-index="5" width="640" height="480" src="figures/health_monitoring.jpeg" style="position:absolute;top:0;left:0;border:0; box-shadow: 0px 0px 0px rgba(255, 255, 255, 255);" />
</div>
</col>
</row>
</section>
<section>
<h3>To Understand, Learn to Predict<h3>
<img class="stretch" style="border:0; box-shadow: 0px 0px 0px rgba(150, 150, 255, 0.8);"
width="100%" src="figures/learning_from_models.svg"
alt="Learning to predict">
</section>
<section data-background="figures/RobotLearningAboutBrain.png">
<!-- <div class="slide-footer" style="font-size:9pt;"> -->
<!-- Graphic created by Farfalla Hu <img src="figures/Farfalla.png" style="border:0; box-shadow: 0px 0px 0px -->
<!-- rgba(150, 150, 255, 0.8); justify-content: center;" -->
<!-- width="60pt"/> -->
<!-- </div> -->
</section>
<section data-state="brightside">
<h3>Graphs as Interpretable Representations</h3>
<row style="margin-top: -30px;">
<col30 class="fragment roll-in" data-fragment-index="1">
<div style="font-size:14pt;">genetic network</div>
<img class="stretch" style="border:0; box-shadow: 0px 0px 0px
rgba(150, 150, 255, 0.8); justify-content: center;"
width="80%"
src="figures/covid_genes.png"
alt="COVID SNPs">
</col30>
<col30 class="fragment roll-in" data-fragment-index="2">
<div style="font-size:14pt;">networks of chess games</div>
<img class="stretch" style="border:0; box-shadow: 0px 0px 0px
rgba(150, 150, 255, 0.8); justify-content: center;"
width="80%"
src="figures/chess_masters_001.png"
alt="chess masters">
</col30>
<col30 class="fragment roll-in" data-fragment-index="3">
<div style="font-size:14pt;">philosophy network</div>
<img class="stretch" style="border:0; box-shadow: 0px 0px 0px
rgba(150, 150, 255, 0.8); justify-content: center;"
width="80%"
src="figures/Philosophy_graph.png"
alt="history of philosophy">
</col30>
</row>
<row style="margin-top: -30px;">
<col30 class="fragment roll-in" data-fragment-index="4">
<div style="font-size:14pt;">W3C specs</div>
<img class="stretch" style="border:0; box-shadow: 0px 0px 0px
rgba(150, 150, 255, 0.8); justify-content: center;"
width="100%"
src="figures/wsa_concepts_www.png"
alt="W3C specs">
</col30>
<col30 class="fragment roll-in" data-fragment-index="5">
<div style="font-size:14pt;">Titanic survival DT</div>
<img class="stretch" style="border:0; box-shadow: 0px 0px 0px
rgba(150, 150, 255, 0.8); justify-content: center;"
width="100%"
src="figures/TitanicDecisionTree.png"
alt="Decision Tree">
</col30>
<col30 class="fragment roll-in" data-fragment-index="6">
<div style="font-size:14pt;">Neural Net</div>
<img class="stretch" style="border:0; box-shadow: 0px 0px 0px
rgba(150, 150, 255, 0.8); justify-content: center;"
width="100%"
src="figures/neural-network-architecture_graph.png"
alt="Neural Net">
</col30>
</row>
<!-- <div class="slide-footer" style="font-size:9pt;"> -->
<!-- 1. Taylor K, Das S, Pearson M, Kozubek J, Pawlowski M, Jensen CE, Skowron Z, Møller GL, Strivens M, Gardner S. Analysis of genetic host response risk factors in severe COVID-19 patients. medRxiv. 2020 Jan 1.<br> -->
<!-- 2. <a href="https://networkx.org/documentation/stable/auto_examples/drawing/plot_chess_masters.html#sphx-glr-auto-examples-drawing-plot-chess-masters-py">Chess Masters in NetworkX</a> -->
<!-- 3. <a href="https://igarciniacambogia.blogspot.com/2020/06/pre-socratic-graph.html">History of Philosophy</a> -->
<!-- 4. <a href="https://www.w3.org/2003/Talks/0818-msm-ws/slide19-0.html">W3C slides</a> -->
<!-- 5. <a hred="https://medium.com/analytics-vidhya/variable-importance-with-tree-models-random-forest-with-python-cb775d019977">DT source</a> -->
<!-- 6. <a href="https://www.syncfusion.com/succinctly-free-ebooks/neuralnetworks/feed-forward">NN source</a> -->
<!-- </div> -->
<aside class="notes">
1. COVID-associated SNPs (circles) and co-association in patients (edges), colours = distinct patient sub-populations.<br>
</aside>
</section>
<section data-state="darkside">
<h3>Neuroimaging data</h3>
<row>
<col50>
<iframe data-autoplay data-preload width="100%" height="250" src="https://www.youtube.com/embed/cQs1g5hsW6Y?autoplay=1&controls=0&rel=0&modestbranding=1&showinfo=0&loop=1&iv_load_policy=3&playlist=cQs1g5hsW6Y" title="Volumetric 3D T1 SPACE on wide bore 3T MRI" frameborder="0" allow="autoplay; clipboard-write; encrypted-media;"></iframe>
<img class="stretch" style="border:0; box-shadow: 0px 0px 0px rgba(150, 150, 255, 0.8);"
width="40%" src="figures/voxel_brain.png"
alt="Voxels">
</col50>
<col50>
<iframe data-autoplay data-preload width="100%" height="250" src="https://www.youtube.com/embed/Mk11oo8CQCQ?autoplay=1&controls=0&rel=0&modestbranding=1&showinfo=0&loop=1&iv_load_policy=3&playlist=Mk11oo8CQCQ" title="Original fMRI movie" frameborder="0" allow="autoplay;encrypted-media;" ></iframe>
<video loop data-autoplay src="figures/rsfmri.mp4"></video>
</col50>
</row>
</section>
<section data-state="brightside">
<h3>Brain as a network</h3>
<row>
<col60>
<ul style="list-style-type: none; font-size: 19pt">
<li class="fragment roll-in" data-fragment-index="1"> Voxels working together form networks
<img class="stretch" style="border:0; box-shadow: 0px 0px 0px rgba(150, 150, 255, 0.8);"
width="100%" src="figures/ica_components.png"
alt="ICA components">
<li class="fragment roll-in" data-fragment-index="2"> dynamically change activity
<img class="stretch" style="border:0; box-shadow: 0px 0px 0px rgba(150, 150, 255, 0.8);"
width="100%" src="figures/time_courses.svg"
alt="ICA TC">
</ul>
</col60>
<col50>
<ul style="list-style-type: none; font-size: 19pt">
<li class="fragment roll-in" data-fragment-index="3"> These networks interact and form ...
<div class="fragment roll-in" data-fragment-index="4">
<img class="stretch" style="border:0; box-shadow: 0px 0px 0px rgba(150, 150, 255, 0.8);"
width="100%" src="figures/brain_connectivity_graph.svg"
alt="ICA components">
more networks
</div>
</ul>
</col50>
</row>
</section>
<section data-state="brightside">
<h3>capturing dynamics of the brain network</h3>
<img class="stretch" style="border:0; box-shadow: 0px 0px 0px rgba(150, 150, 255, 0.8);"
width="100%" src="figures/Allen_dFNC.png"
alt="Allen">
<!-- <div class="slide-footer"> -->
<!-- Allen EA, Damaraju E, Plis SM, Erhardt EB, Eichele T, Calhoun VD. Tracking whole-brain connectivity dynamics in the resting state. Cerebral cortex. 2014 Mar 1;24(3):663-76. -->
<!-- </div> -->
</section>
<section>
<h3>Problems with windowing</h3>
<ul>
<li class="fragment roll-in"> Limiting data for estimation <i class="fas fa-arrow-alt-circle-right"></i> noisier estimates
<img class="stretch" style="border:0; box-shadow: 0px 0px 0px rgba(150, 150, 255, 0.8);"
width="50%" src="figures/windowed_time_courses.svg"
alt="windowed">
<li class="fragment roll-in"> Fixed-shape soft windows: unclear what is important
<img class="stretch" style="border:0; box-shadow: 0px 0px 0px rgba(150, 150, 255, 0.8);"
width="50%" src="figures/windowed_time_courses_N.svg"
alt="windowed too">
<li class="fragment roll-in"> The same window shape for each time point
<li class="fragment roll-in"> The same window regardless the downstream task
<li class="fragment roll-in"> <alert>Our solution: learn an adaptive window function</alert>
</ul>
</section>
<section>
<h3>Two types of eXplainable AI</h3>
<row>
<col50>
<h4>Explain the data</h4>
<img class="stretch" style="border:0; box-shadow: 0px 0px 0px rgba(150, 150, 255, 0.8);"
width="100%"
src="figures/explanation_examples.svg"
alt="task">
</col50>
<col50>
<h4>Interpret the model</h4>
<img class="stretch" style="border:0; box-shadow: 0px 0px 0px rgba(150, 150, 255, 0.8);"
width="100%"
src="figures/PCA_spring.gif"
alt="task">
</col50>
</row>
</section>
<section>
<h3>Graph as a glass-box layer</h3>
<ul style="list-style-type: none; font-size: 19pt">
<li class="fragment roll-in"> To see through a black box model we need a window
<div align="center">
<img class="stretch" style="border:0; box-shadow: 0px 0px 0px rgba(150, 150, 255, 0.8);position: relative; margin:0 auto;"
width="50%" src="figures/glass_box_into_mouse.png"
alt="windowed">
</div>
<li class="fragment roll-in"> Our window is a graph, but we want the model to use it too
<div align="center">
<img class="stretch" style="border:0; box-shadow: 0px 0px 0px rgba(150, 150, 255, 0.8);"
width="50%" src="figures/glass_box_layer.svg"
alt="windowed">
</div>
<li class="fragment roll-in"> For that, the graph needs to be differentiable
\[
\softmax(\bm{H}\bm{W}_Q\bm{W}_K\bm{H}^T), \bm{H}\mbox{ is (brain regions)} \times d
\]
<li class="fragment roll-in">
<blockquote style="background-color: #eee8d5; width: 100%; font-size: 32px; text-align: left;">
The now familiar <alert>alignment matrix</alert> from the self-attention module
</blockquote>
</ul>
</section>
<section>
<h3>assembling a transparent model</h3>
<row style="margin-top: -30px;" >
<col40>
<ul style="list-style-type: none; font-size: 19pt">
<li class="fragment roll-in">
<img class="stretch" style="border:0; box-shadow: 0px 0px 0px
rgba(150, 150, 255,
0.8);" width="80%" src="figures/attention_distill.png"
alt="DECENNT">
</ul>
</col40>
<col60>
<img class="stretch" style="border:0; box-shadow: 0px 0px 0px
rgba(150, 150, 255,
0.8);" width="80%" src="figures/glacier_model.svg"
alt="DECENNT">
</col60>
</row>
</section>
<section>
<h3>Results: Interpretable glass-box layer <alert>for brains</alert></h3>
<row style="margin-top: -30px;">
<div align="center">
<img class="stretch" style="border:0; box-shadow: 0px 0px 0px rgba(150, 150, 255,
0.8);"
width="80%"
src="figures/taskENC.svg"
alt="task">
</div>
</row>
<row style="margin-top: -30px;">
<div align="center">
<img class="stretch" style="border:0; box-shadow: 0px 0px 0px
rgba(150, 150, 255,
0.8);"
width="70%"
src="figures/FBIRNConnect.svg"
alt="task">
</div>
</row>
<row>
<col50>
</col50>
<col50>
</col50>
</row>
</section>
<section>
<h3>Results: transparency improves model accuracy</h3>
<img class="stretch" style="border:0; box-shadow: 0px 0px 0px
rgba(150, 150, 255,
0.8);" width="80%" src="figures/Glassier_AUC_Comparison.svg"
alt="Glacier">
<div align="center">
<img class="stretch" style="border:0; box-shadow: 0px 0px 0px rgba(150, 150, 255, 0.8);"
width="50%" src="figures/glass_box_layer.svg"
alt="windowed">
</div>
</section>
<section>
<h3>Take home points</h3>
<ol>
<li class="fragment roll-in"> Glass-box differentiable layer captures instantaneous dynamic connectivity
<li class="fragment roll-in"> Learning a graph across variables and importance of each time point simplifies model interpretation
<li class="fragment roll-in"> A model almost entirely made of self-attention is a highest accuracy model that is also fully interpretable
</ol>
</section>
<section>
<h1>Questions?</h1>
<img style="border:0; box-shadow: 0px 0px 0px
rgba(150, 150, 255,
0.8);" width="400"
src="figures/QR_Code_GlacierICASSP2023.svg"
alt="Neuroneural">
</section>
</section>
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