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<!DOCTYPE HTML>
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<title>CMICHACKS is a hackathon hosted by CMIC @ UCL</title>
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<h1> 10-11th November 2022 </h1>
<p>CMICHACKS is a hackathon hosted by CMIC @ UCL<br />
Organised by <a href="https://tobyUCL.github.io">Toby</a>, <a href="https://scholar.google.co.uk/citations?hl=en&user=516DHbgAAAAJ">Snigdha</a>, <a href="https://sea-shunned.github.io/">Cameron</a>, <a href="https://iris.ucl.ac.uk/iris/browse/profile?upi=AJLEG92">Antoine</a>, <a href="http://manifold.cs.ucl.ac.uk/author/james-h-cole/">James</a>, and <a href="https://scholar.google.co.uk/citations?user=rqSiS9EAAAAJ&hl=en&oi=ao">Laura</a>.</p>
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<li><a href="#intro" class="active">What's a hackathon?</a></li>
<li><a href="#program">Program</a></li>
<li><a href="#projects">List of Projects</a></li>
<li><a href="#second">Location</a></li>
<li><a href="#cta">REGISTER!!!</a></li>
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<h2>What's a hackathon?</h2>
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<p>A hackathon is an event, usually lasting a few days, during which participants are invited to use their programming skills to collaborate on one or more projects.</p>
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<section id="program" class="main">
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<h2>Program</h2>
</header>
<section class="main">
<div class="schedule" aria-labelledby="schedule-heading">
<span class="track-slot" aria-hidden="true" style="grid-column: track-1; grid-row: tracks;">Day 1 (10/11/2022)</span>
<span class="track-slot" aria-hidden="true" style="grid-column: track-2; grid-row: tracks;">Day 2 (11/11/2022)</span>
<h2 class="time-slot" style="grid-row: time-0900;">9:00</h2>
<h2 class="time-slot" style="grid-row: time-1000;">10:00</h2>
<h2 class="time-slot" style="grid-row: time-1100;">11:00</h2>
<h2 class="time-slot" style="grid-row: time-1200;">12:00</h2>
<h2 class="time-slot" style="grid-row: time-1300;">13:00</h2>
<h2 class="time-slot" style="grid-row: time-1400;">14:00</h2>
<h2 class="time-slot" style="grid-row: time-1500;">15:00</h2>
<h2 class="time-slot" style="grid-row: time-1600;">16:00</h2>
<h2 class="time-slot" style="grid-row: time-1700;">17:00</h2>
<h2 class="time-slot" style="grid-row: time-0930;">9:30</h2>
<h2 class="time-slot" style="grid-row: time-1030;">10:30</h2>
<h2 class="time-slot" style="grid-row: time-1130;">11:30</h2>
<h2 class="time-slot" style="grid-row: time-1230;">12:30</h2>
<h2 class="time-slot" style="grid-row: time-1330;">13:30</h2>
<h2 class="time-slot" style="grid-row: time-1430;">14:30</h2>
<h2 class="time-slot" style="grid-row: time-1530;">15:30</h2>
<h2 class="time-slot" style="grid-row: time-1630;">16:30</h2>
<div class="session session-1 track-1" style="grid-column: track-1; grid-row: time-1000 / time-1100;">
<h3 style="color:white;" class="session-title">Welcome session + project leaders presentations</h3>
<span class="session-time">10:00 - 11:00</span>
<span class="session-track"><a href="#JBR">Jeremy Bentham room - UCL main campus</a></span>
<span class="session-presenter"></span>
</div>
<div class="session session-2 track-2" style="grid-column: track-1; grid-row: time-1100 / time-1200;">
<h3 style="color:white;" class="session-title">Hacking</h3>
<span class="session-time">11:00 - 12:00</span>
<span class="session-track"><a href="#JBR">Jeremy Bentham room - UCL main campus</a></span>
<span class="session-presenter"></span>
</div>
<div class="session session-3 track-2" style="grid-column: track-2; grid-row: time-0900 / time-1030;">
<h3 style="color:white;" class="session-title">Hacking</h3>
<span class="session-time">9:00 - 10:30</span>
<span class="session-track"><a href="#JBR">Jeremy Bentham room - UCL main campus</a></span>
<span class="session-presenter"></span>
</div>
<div class="session session-4 track-2" style="grid-column: track-2; grid-row: time-1045 / time-1200;">
<h3 style="color:white;" class="session-title">Hacking</h3>
<span class="session-time">10:45 - 12:00</span>
<span class="session-track"><a href="#JBR">Jeremy Bentham room - UCL main campus</a></span>
<span class="session-presenter"></span>
</div>
<div class="session session-5 track-all" style="grid-column: track-1; grid-row: time-1200 / time-1300;">
<h3 style="color:white;" class="session-title">lunch break</h3>
</div>
<div class="session session-6 track-all" style="grid-column: track-2; grid-row: time-1200 / time-1300;">
<h3 style="color:white;" class="session-title">lunch break</h3>
</div>
<div class="session session-7 track-2" style="grid-column: track-1; grid-row: time-1300 / time-1500;">
<h3 style="color:white;" class="session-title">Hacking</h3>
<span class="session-time">13:00 - 15:00</span>
<span class="session-track"><a href="#JBR">Jeremy Bentham room - UCL main campus</a></span>
<span class="session-presenter"></span>
</div>
<div class="session session-8 track-2" style="grid-column: track-1; grid-row: time-1515 / time-1700;">
<h3 style="color:white;" class="session-title">Hacking</h3>
<span class="session-time">15:15 - 17:00</span>
<span class="session-track"><a href="#JBR">Jeremy Bentham room - UCL main campus</a></span>
<span class="session-presenter"></span>
</div>
<div class="session session-9 track-2" style="grid-column: track-2; grid-row: time-1300 / time-1430;">
<h3 style="color:white;" class="session-title">Hacking</h3>
<span class="session-time">13:00 - 14:30</span>
<span class="session-track"><a href="#JBR">Jeremy Bentham room - UCL main campus</a></span>
<span class="session-presenter"></span>
</div>
<div class="session session-10 track-2" style="grid-column: track-2; grid-row: time-1445 / time-1630;">
<h3 style="color:white;" class="session-title">Hacking</h3>
<span class="session-time">14:45 - 16:30</span>
<span class="session-track"><a href="#JBR">Jeremy Bentham room - UCL main campus</a></span>
<span class="session-presenter"></span>
</div>
<div class="session session-11 track-all" style="grid-column: track-1; grid-row: time-1500 / time-1515;">
<h3 style="color:white;" class="session-title">coffee break</h3>
</div>
<div class="session session-12 track-all" style="grid-column: track-2; grid-row: time-1030 / time-1045;">
<h3 style="color:white;" class="session-title">coffee break</h3>
</div>
<div class="session session-13 track-all" style="grid-column: track-2; grid-row: time-1430 / time-1445;">
<h3 style="color:white;" class="session-title">coffee break</h3>
</div>
<div class="session session-14 track-4" style="grid-column: track-1; grid-row: time-1700 / time-1800;">
<h3 style="color:white;" class="session-title">Socializing</h3>
<span class="session-time">17:00 - all night</span>
<span class="session-track"><a href="#90HH">Function room - 90 High Holborn</span></a>
<span class="session-presenter"></span>
</div>
<div class="session session-15 track-4" style="grid-column: track-2; grid-row: time-1700 / time-1800;">
<h3 style="color:white;" class="session-title">Socializing</h3>
<span class="session-time">17:00 - all night</span>
<span class="session-track"><a href="#90HH">Function room - 90 High Holborn</span></a>
<span class="session-presenter"></span>
</div>
<div class="session session-15 track-3" style="grid-column: track-2; grid-row: time-1630 / time-1700;">
<h3 style="color:white;" class="session-title">Closing session</h3>
<span class="session-time">,   16:30 - 17:00,   </span>
<span class="session-track"><a href="#JBR">Jeremy Bentham room - UCL main campus</a></span>
<span class="session-presenter"></span>
</div>
</section>
</section>
<!-- Table -->
<section id="projects" class="main">
<header class="major">
<h2>List of Projects</h2>
</header>
<p>A list of currently proposed projects for the hackathon, along with links to the
proposer and the research group they're in for more background.</p>
<p>We're looking for more projects! If you have an idea you'd like to propose then email me at tobias.goodwin-allcock ucl.ac.uk</p>
<section class="main">
<div class="table-wrapper">
<table>
<colgroup>
<col span="1" style="width: 15%;">
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<col span="1" style="width: 3%;">
<col span="1" style="width: 65%;">
</colgroup>
<thead>
<tr>
<th>Project Title</th>
<th>Project Leader</th>
<th>Research Group</th>
<th>Project Description</th>
</tr>
</thead>
<tbody>
<tr>
<td>Lung and placenta united</td>
<td><a href="https://github.com/ashkanpakzad ">Ashkan Pakzad</a>, Paddy Slator</td>
<td><a href="https://satsuma.cs.ucl.ac.uk/">Satsuma</a>, MIG</td>
<td>This project will transfer knowledge, ideas, and techniques between the similar but different domains of lung and placenta imaging. We'll apply AirQuant to derive lower-dimensional representations of synthetic placental vascular trees, segment vessels in placental microCT with techniques designed for airways, calculate susceptibility maps from airway segmentations, and calculate flow and transport maps in the lungs.</td>
</tr>
<tr>
<td>Deploying Cloud ML Models Using Microsoft InnerEye</td>
<td>Tom Dowrick, <a href="https://www.haroonrchughtai.com/">Haroon Chugthai</a></td>
<td><a href="http://compass.cs.ucl.ac.uk/">COMPASS, </a><a href="https://www.ucl.ac.uk/arc/">ARC</a></td>
<td><a href="https://github.com/microsoft/InnerEye-DeepLearning">Microsoft InnerEye</a> provides a suite of tools for training and developing medical imaging algorithms either using local computing resources or Azure cloud services, and there are many algorithms/models developed within CMIC that would benefit from compatiblity with InnerEye, to allow for more widespread deployment.
<br /> During this hackathon project, participants will convert existing models developed within CMIC to use PyTorch Lightning, and then deploy them on the cloud using InnerEye. Pariticipants will also work to deploy a local InnerEye environment using existing CMIC computing resources, to provide a testing/development environment that can be used in the longer term, to prepare models before they are deployed on the cloud.</td>
</tr>
<tr>
<td>Towards Robust Machine Learning: Debiasing Neural Networks via Mixtures of Experts</td>
<td><a href="https://github.com/moucheng2017">Moucheng Xu</a></td>
<td><a href="https://satsuma.cs.ucl.ac.uk/">Satsuma</a></td>
<td>Robustness and safeness of machine learning models are very important in applications such as medical imaging. However, the current machine learning models are vulnerable against out-of-the-distribution noises. In this project, we will have two adversarial objectives. One objective will focus on how to design "worse" out-of-the-distribution noises to attack the networks (e.g. extreme class imbalance tailored to data distribution, extreme augmentation, et al). The other objective will focus on how to design "better" mixtures of expert's based neural network models (e.g. probabilistic mixtures or stochastic mixtures) to protect the networks. Due to the time limit, we will evaluate the progress on a synthetic image segmentation task with MNIST. The project will be based on Python and Pytorch.</td>
</tr>
<tr>
<td>Simulating diffusion microstructural models </td>
<td>Leevi Kerkela</td>
<td>??</td>
<td>An early version of a Python package that enables efficient simulations of microstructural models has been previously developed. The goal during the hackathon will be to turn it into a proper software package by adding features, fixing bugs, writing tests, writing documentation and use case examples. If the resulting software package is of sufficient quality, the hackathon can be followed by paper submission to, for example, JOSS like with my previous software package: https://doi.org/10.21105/joss.02527. To participate, it is required to know Python, git, and basic linear algebra. Used technologies include Python, NumPy, JAX, Sphinx, and PyTorch.</td>
</tr>
<tr>
<td>Comparing different Alzheimer's models </td>
<td>Cameron Shand</td>
<td><a href="https://ucl-pond.github.io/">POND</a></td>
<td>Are Alzheimer's disease progression models comparable? This hackathon project is part hacking, part research. Disease progression modelling has become increasingly popular with numerous different groups developing computational models and releasing the source code. The code can be challenging to run, which may explain why systematic comparison of selected models is yet to be conducted. This project will provide the code for multiple disease progression models, along with pre-prepared data from the Alzheimer's Disease Neuroimaging Initiative, among others. The challenge will be to get the code running, document the steps necessary for each, and perform a direct comparison of results from competing disease progression models.</td>
</tr>
<tr>
<td>Metrics Reloaded stress testing and GUI development</td>
<td>Neil Oxtoby</td>
<td><a href="https://ucl-pond.github.io/">POND</a></td>
<td>The Metrics Reloaded consortium has recently provided a list of reference metrics to use in the evaluation and validation of classification, object detection, instance segmentation and semantic segmentation. Adequate implementation of these evaluation processes and metrics is essential to ensure appropriate use and promote best practice. Most of the code for such an evaluation suite has already been developed but requires some stress-testing of edge cases. Another aspect of this project will be the creation of a GUI to help in the final choice of metrics to be employed in an evaluation setting.</td>
</tr>
<tr>
<td>Deep learning of quantitative MRI parameters</td>
<td><a href="https://csparker.github.io/about/">Chris Parker</a></td>
<td><a href="https://cig-ucl.github.io/">CIG</a></td>
<td>Unsupervised deep learning promises fast and efficient estimation of quantitative MRI parameters. Yet, current approaches are sub-optimal for data with low SNR, as they incorrectly assume that image noise is gaussian. <br />
This project aims to incorporate a rician distribution-based likelihood into the cost function of deep learning algorithms. We will demonstrate the approach using the IVIM diffusion model, which relies on low SNR DWI data, as an example. If there is time, we can also extend it to a Bayesian framework. </td>
</tr>
<tr>
<td>Simulating susceptibility-induced distortion fields.</td>
<td><a href="https://scholar.google.com/citations?user=iV_JtqIAAAAJ&hl=fr">Antoine Legouhy</a></td>
<td><a href="https://cig-ucl.github.io/">CIG</a></td>
<td>Echo planar imaging (EPI) is the most common approach for diffusion and functional MRI, but it produces images with severe geometric distortions due to susceptibility-induced B0 field inhomogeneities.
The state-of-the-art tool to correct these distortions is FSL TOPUP, which is slow. Recently, deep-learning techniques have been developed allowing tremendous processing time reductions. However, the multifarious acquisition settings of EPI leads to a large variety of possible contrasts, and those methods generalize very poorly.
The use of generative models to produce synthetic MR images from segmentations have been proposed to produce training sets with a large variety of contrasts (even beyond the scope of realistic!), thus allowing contrast-agnostic models. But for distortion correction, we also need to produce synthetic distortion fields. <br />
Some algorithms are capable of producing whole head segmentations notably differentiating soft tissues / hard tissues and air / tissue. The idea of this project would be to see how we could train a model to learn the relationship between those segmentations and associated distortion fields in order to produce synthetic distortion fields.
</tr>
<tr>
<td>Advanced Visualisation for Augmented Reality Surgery</td> <!-- title -->
<td>
<a href="https://thompson318.github.io/">Stephen Thompson</a>
<a href="https://github.com/tdowrick">Tom Dowrick</a>
<a href="http://mxochicale.github.io/">Miguel Xochicale</a>
</td> <!-- who -->
<td><a href="https://github.com/SciKit-Surgery">SciKit-Surgery</a></td> <!-- group -->
<td>Successful deployment of augmented reality into surgery depends on
visualisation methods that can accurately convey essential information without
distracting the surgeon.
We have developed <a href="https://github.com/SciKit-Surgery/scikit-surgeryvtk"/>SciKit-SurgeryVTK</a>
to provide visualisation for our research and teaching in augmented reality
surgery. SciKit-SurgeryVTK is open source software and is being adopted
by other research groups.<br/>
In this project we will implement advanced rendering methods
within SciKit-SurgeryVTK to support our ongoing research. Specifically we
want to implement an outline renderer similar to
<a href="https://kitware.github.io/vtk-examples/site/Cxx/PolyData/Silhouette/">this example</a>,
as we have previously shown this to be effective during
<a href="https://link.springer.com/article/10.1007/s11548-018-1761-3">augmented keyhole</a>
surgery.
We would also welcome your suggestions for visualisation methods.<br/>
In this project you have the opportunity to gain familiarity with the widely
used VTK library and contribute to research efforts in augmented reality for surgery.</td> <!-- Description -->
</tr>
<tr>
<td>Tracking Hardware Interfaces for Image Guided Surgery</td> <!-- title -->
<td>
<a href="https://thompson318.github.io/">Stephen Thompson</a>
<a href="https://github.com/tdowrick">Tom Dowrick</a>
<a href="http://mxochicale.github.io/">Miguel Xochicale</a>
</td> <!-- who -->
<td><a href="https://github.com/SciKit-Surgery">SciKit-Surgery</a></td> <!-- group -->
<td>Image guided surgery depends on optical and electromagnetic tracking
systems to track surgical tools and cameras. Research in image guided surgery depends
on easily configured, useable, and open source software to interface with these
tracking systems. <br/>
We have previously developed
<a href="https://github.com/SciKit-Surgery/scikit-surgerynditracker">SciKit-SurgeryNDITracker</a>
to interface with <a href="https://www.ndigital.com/">Northern Digital's</a>
range of optical and electromagnetic tracking
systems. SciKit-SurgeryNDITracker has been adopted by several research groups
globally due to its ease of deployment and use. In this project we will develop
a new library with the same external API to talk to a new tracking system, for
example the <a href="https://www.atracsys-measurement.com/">Atracsys</a>
family of trackers. <br/>
This project gives you the opportunity to develop a library likely to be
adopted by a wide and global user base of researchers in image guided surgery and
beyond.
</td> <!-- Description -->
</tr>
</tbody>
</table>
</div>
</section>
</section>
<!-- Location -->
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<header class="major">
<h2>Location</h2>
<p id="JBR">The hackathon is a purely in person event which will take place in:<br />
Jeremy Bentham Room (JBR),
Wilkins Building (main building),<br />
UCL main campus, Gower St, London WC1E 6AE.</p>
</header>
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<p id="90HH">At the end of day, participants are invited to socialize with food and drinks in:<br />
Function room and reception area,
1st floor,<br />
90 High Holborn, London WC1V 6LJ.</p>
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