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<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no">
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<title>Neuroimaging On The Edge (NOTE)</title>
<meta name="description" content="University of South Carolina Hackathon">
<meta name="author" content="Sergey M Plis">
<meta name="apple-mobile-web-app-capable" content="yes">
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<body>
<div class="reveal">
<!-- In between the <div="reveal"> and the <div class="slides">-->
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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>AI <alert>NOTE</alert>: AI <alert>N</alert>euroimaging <alert>O</alert>n <alert>T</alert>he <alert>E</alert>dge</h2>
<h4><i class="fa-solid fa-at"></i> Edge Machine Learning for Brain Imaging Hackathon 2023</h4>
<p>
Sergey Plis, PhD
<p><small>
Associate Professor, CS GSU
</small>
<p>
<p>
<p>
<p>
</section>
</section>
<section>
<section>
<h1>Vision</h1>
</section>
<section>
<h2>Scaling Laws</h2>
<row style="width:150%; margin-left: -220px">
<col50>
<img src="figures/FLOPs_scale.png" alt="FLOPS scale" style="border:0; box-shadow: 0px 0px 0px rgba(150, 150, 255, 1); margin-bottom: 120px; margin-top: -30px; margin-left: 0px; width: 100%" class="stretch">
</col50>
<col50>
<img src="figures/Params_scale.png" alt="Params scale" style="border:0; box-shadow: 0px 0px 0px rgba(150, 150, 255, 1); margin-bottom: 10%; margin-top: -10px; width: 100%" class="stretch">
</col50>
</row>
<div class='slide-footer' style="text-align: left;">
<a href="https://arxiv.org/abs/2005.14165">Language Models are Few-Shot Learners</a> 2020<br>
<a href="https://arxiv.org/abs/1712.00409">Deep Learning Scaling is Predictable, Empirically</a> 2017
</div>
</section>
<section data-background="figures/foundational_parameters.png" data-background-size="contain" data-vertical-align-top>
<h2 style="text-shadow: 10px 10px 10px #002b36; color: #fff3e3;">Foundational Models</h2>
</section>
<section data-background="figures/Corporate_foundational.png" data-background-size="contain" data-vertical-align-top>
<h2 style="text-shadow: 10px 10px 10px #002b36; color: #fff3e3;">Corporate Controlled AI</h2>
</section>
<section>
<h2>But what about privacy?</h2>
</section>
<section data-background="figures/COINSTAC_vision.png" data-background-size="contain" data-vertical-align-top>
<h2>coinstac.org</h2>
</section>
<section>
<h2>coinstac.org</h2>
Bring Computation to the user
</section>
<section>
<h2>AI NOTE</h2>
Bring AI models to the user <em>in their Browser</em>
</section>
<section>
<h2>Problems to be addressed</h2>
<ul>
<li class="fragment roll-in"> Segmentation
<li class="fragment roll-in"> Super-Resolution
<li class="fragment roll-in"> Synthetic MRI generation
<li class="fragment roll-in"> Tumor segmentation
<li class="fragment roll-in"> Surface Generation
</ul>
</section>
<section>
<h2>Essential model traits</h2>
<ul>
<li class="fragment roll-in"> Small number of weights
<li class="fragment roll-in"> Minimal memory footprint
<li class="fragment roll-in"> Powerful induction bias
<li class="fragment roll-in"> Transparency and interpretability
</ul>
</section>
</section>
<section>
<section>
<h1>model</h1>
<h3>Meshnet</h3>
</section>
<section>
<h2>the task</h2>
<img class="stretch" style="border:0; box-shadow: 0px 0px 0px
rgba(150, 150, 255, 0.8);" width="250"
src="figures/tasks.png" alt="david">
</section>
<section>
<h2>state of the art: freesurfer</h2>
<img class="stretch" style="border:0; box-shadow: 0px 0px 0px
rgba(150, 150, 255, 0.8);" width="250"
src="figures/freesurfer.png" alt="david">
<br>
<div class='slide-footer' style="text-align: left;">
Dale et al. Cortical surface-based analysis. I. Segmentation and
surface reconstruction. Neuroimage 1999
</div>
</section>
<section>
<h2>deep learning standard: U-net</h2>
<img class="stretch" style="border:0; box-shadow: 0px 0px 0px
rgba(150, 150, 255, 0.8);" width="250"
src="figures/unet_arc.png" alt="david">
<p>
<div class='slide-footer' style="text-align: left;">
Ronneberger et al. U-net: Convolutional networks
for biomedical image segmentation. MICCAI 2015<br>
Çiçek et al. 3D U-Net: learning dense volumetric
segmentation from sparse annotation. MICCAI 2016
</div>
</section>
<section>
<h2>deep learning standard: U-net</h2>
<img class="stretch" style="border:0; box-shadow: 0px 0px 0px
rgba(150, 150, 255, 0.8);" width="250"
src="figures/unet_table.png" alt="david">
<p>
<div class='slide-footer' style="text-align: left;">
Ronneberger et al. U-net: Convolutional networks for
biomedical image segmentation. MICCAI 2015<br>
Çiçek et al. 3D U-Net: learning dense volumetric
segmentation from sparse annotation. MICCAI 2016
</div>
</section>
<section>
<div id="header-right" style="right: -21%; z-index: 1500;">
<img width="200px" style="margin-bottom: -5%;"
src="figures/AlexFedorov.jpg" alt="Alex"><br>
<small>Alex Fedorov</small>
</div>
<h2>our model: Meshnet</h2>
<img src="figures/dilation.png" style="border:0; box-shadow: 0px
0px 0px rgba(150, 150, 255, 0.8);float: left;"
alt="dilated" width="40%">
<img src="figures/mn1.png" style="border:0; box-shadow: 0px
0px 0px rgba(150, 150, 255, 0.8);float: right;"
alt="Websocket" width="35%"
>
<div class="ulist">
<ul style="width: 55%; float: left; font-size:30px;">
<li>Gray and White matter</li>
<li>FreeSurfer for ground truth</li>
<li>T1 MRIs from HCP</li>
<li>GitHub <br>
<small>
<a href="https://github.com/Entodi/MeshNet">
https://github.com/Entodi/MeshNet</a>
</small>
</li>
</ul>
</div>
<br>
<div class='slide-footer' style="text-align: left;">
Fedorov et al. End-to-end learning of brain tissue segmentation
from imperfect labeling. IJCNN 2017
</div>
</section>
<section>
<h2>Meshnet</h2>
<img src="figures/mntable.png" style="border:0; box-shadow: 0px
0px
0px rgba(150, 150, 255,
0.8);float: right;"
alt="Websocket" width="60%" >
<div>
<ul style="width: 30%; float: left;
font-size:30px;">
<li>72516 vs. 23523355</li>
<li>600kb vs. 2Gb</li>
</ul>
</div>
<p>
<br>
<div class='slide-footer' style="text-align: left;">
Fedorov et al. End-to-end learning of brain tissue segmentation
from imperfect labeling. IJCNN 2017
</div>
</section>
<section>
<h2>Meshnet</h2>
<img src="figures/mnpipeline.png" style="border:0; box-shadow: 0px
0px 0px rgba(150, 150, 255, 0.8);"
alt="Websocket" class="stretch"
>
<br>
<div class='slide-footer' style="text-align: left;">
Fedorov et al. End-to-end learning of brain tissue segmentation
from imperfect labeling. IJCNN 2017
</div>
</section>
<section>
<h2>Meshnet</h2>
<img src="figures/mnexample.png" style="border:0; box-shadow: 0px
0px 0px rgba(150, 150, 255, 0.8);"
alt="Websocket" class="stretch"
>
<br>
<div class='slide-footer' style="text-align: left;">
Fedorov et al. End-to-end learning of brain tissue segmentation
from imperfect labeling. IJCNN 2017
</div>
</section>
<section>
<h3>better than the teacher</h3>
<img class="stretch" style="border:0; box-shadow: 0px 0px 0px
rgba(150, 150, 255, 0.8);" width="700"
src="figures/MN_examples.png" alt="loop">
</section>
<section>
<h3>better than the human (sometimes)</h3>
<img class="stretch" style="border:0; box-shadow: 0px 0px 0px
rgba(150, 150, 255, 0.8);" width="700"
src="figures/mnhuman.png" alt="loop">
</section>
<section>
<h3>better than U-net</h3>
<img class="stretch" style="border:0; box-shadow: 0px 0px 0px
rgba(150, 150, 255, 0.8);"
src="figures/mnvsunet.png" alt="loop">
</section>
<section>
<h3>an easy segmentation model</h3>
<div class="ulist">
<ul style="width: 60%; float:
center;">
<li>compact and portable</li>
<li>universal and adaptable</li>
<li>code:
<a href="https://github.com/Entodi/MeshNet">
https://github.com/Entodi/MeshNet</a>
</li>
</ul>
</div>
</section>
<section data-background-video="figures/brainchopV1_3.mp4">
</section>
</section>
<section>
<h2>Team that's working on this</h2>
<img class="stretch" style="border:0; box-shadow: 0px 0px 0px
rgba(150, 150, 255, 0.8);" width="800"
src="figures/the_team23.svg" alt="loop">
</section>
</div>
</div>
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