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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<title>SDAIH 2022</title>
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<section id="home">
<div class="content">
<img id="home_image" src="img/background.jpg" alt="Background image">
<p class="intro">Workshop for IJCAI–ECAI 2022 <br> Saturday July 23rd, Vienna <br><br></p>
<p class="home_title">Scarce Data in Artificial Intelligence for Healthcare
(SDAIH) <br></p>
<p class="sub_title">
</p>
<a href="#acceptedpapers" class="call_to_action_big">Accepted Papers ▶</a>
</div>
</section>
<section id="about">
<div class="content">
<p class="section_title">About</p>
<div id="about-container">
<div class="about-container-item">
<p class="about-container-item-text">
The goal of this workshop is to exchange learnings and efforts on how to solve the issue of
data scarcity for the practical deployment of AI in healthcare. We aim at bringing together,
from both academia and industry, researchers and data scientists that are confronted with challenges
related to limited data availability for machine learning in medicine, medical engineering,
biotechnology, pharmaceuticals, and medical services.
</p>
<p class="about-container-item-text">
<img id="about-image" src="img/about.jpeg" alt="About image">
</p>
</div>
</div>
</div>
</section>
<section id="callforpapers">
<div class="content">
<p class="section_title">Call for papers</p>
<div id="callforpapers-container">
<div class="callforpapers-container-item">
<p class="callforpapers-container-item-title">Introduction</p>
<p class="callforpapers-container-item-text">
AI has the potential to generate a revolution in the field of healthcare by enabling accurate, fast and
reliable analyses of data at an unprecedented scale both in the clinics and in industry. Leveraged properly,
AI can thus allow to better meet patient needs by developing new medical devices, drugs, and personalized
treatments, while simultaneously freeing up time for clinical staff to nourish the profound human connection
between caregivers and patients. Moreover, AI promises to democratize the healthcare system by spreading
basic services to low-income or remote areas through telemedicine. <br><br>
Notwithstanding the terrific progress achieved in the last two decades, many AI projects related to medicine
struggle to make their way to deployment and sustainable productivity because of the limited availability of
high-quality annotated data. The scarcity of useful information is often exacerbated in medicine, medical
engineering, and healthcare in general because labelling requires highly-specialized staff, patient privacy
must be respected, ethnic differences and rare diseases adequately represented. Despite the incredible
advances of the last few years in facilitating data collection and annotation, learning representations, and
detecting different types of bias, basic observations on implications for practitioners are often lacking,
new ingenious ideas are flourishing, and recommendations for healthcare are far from established. <br><br>
</p>
<p class="callforpapers-container-item-title">Topics of interest</p>
<p class="callforpapers-container-item-text">
Topics of interest include, but are not limited to: <br>
<img id="callforpapers-image" src="img/arrow.png" alt="Topics image">
</p>
<ul class="callforpapers-container-item-list">
<li>Publication of datasets relevant to healthcare including text, images, audio and structured
data.</li>
<li>Hardware and software tools for enabling data acquisition in low-resource or restricted
environments, such as federated annotations and pseudonimization techniques.</li>
<li>Tools to produce or evaluate high-quality clinical annotations and consensus diagnoses.</li>
<li>Critical analysis of iterative procedures to clean up or refine annotations, as well as
guidelines to assess the uncertainty on metric scores.</li>
<li>Anonymization methods for intra- and inter-institutional data exchange.</li>
<li>Technical solutions to work in the presence of legal concerns, for instance federated learning
and i2b2.</li>
<li>Works on learning representations or transfer learning with a focus on improving model
generalization across different patient cohorts, data acquisition conditions, medical expert
evaluations etc.</li>
<li>Studies which compare or combine learning from nature with learning from human experts.</li>
<li>Works on unsupervised, self-supervised, semi-supervised, or few-shot learning aimed e.g.
at reducing the need for annotations by specialists.</li>
<li>Methods to deal with strongly imbalanced datasets such as those including rare diseases,
or very small pathological features in medical image collections.</li>
<li>Strategies to handle scarcity of subsets in large datasets, i.e. “filling the gaps”.</li>
<li>Works on using public or artificially-generated datasets to improve the performance of
machine-learning models in healthcare or to mitigate (patient) privacy issues.</li>
<li>Case studies linked to the practical deployment of AI in a clinical setting or in medical
devices with limited data, as well as to the construction of pipelines or databases for
addressing data scarcity.</li>
<li>Insightful, original analyses of reasons for the failure of AI projects in healthcare, and
work-in-progress reports of efforts related to the themes listed above.</li>
</ul>
<br><br>
</p>
<p id="submission" class="callforpapers-container-item-title">Submission</p>
<p class="callforpapers-container-item-text">We welcome the submission of original research reports within the
topics of interest of the
workshop. The maximum length of papers is fixed to <strong>6 pages including references.</strong> We
especially
encourage the contribution of case studies, work in progress, position papers, and critical
analyses of failed projects. <br><br>
Accepted papers will be published as proceedings with SciTePress and submitted for indexation by dblp, Scopus, SemanticScholar, Google Scholar and Microsoft Academic. <br><br>
<b>SciTePress templates: </b>
<a href="https://www.scitepress.org/ProceedingsX/templates/SCITEPRESS_Conference_Latex.zip" class="">LaTex,</a>
<a href="https://www.scitepress.org/ProceedingsX/templates/SCITEPRESS_Conference_MSWord.zip" class="">MS Word</a> <br><br>
<p class="callforpapers-container-item-text">
<del><strong>Paper submission deadline:</strong> May 13, 2022 <br></del>
<del><strong>Paper submission deadline extended:</strong> May 20, 2022 <br></del>
<del><strong>Decision notification:</strong> June 3, 2022<br></del>
<del><strong>Camera-ready submission:</strong> June 17, 2022<br></del><br>
All deadlines correspond to the end of the indicated day Anywhere on Earth (AoE).<br> <br>
</p>
<a href="https://cmt3.research.microsoft.com/SDAIH2022" class="call_to_action_medium">Access your contribution via CMT</a>
</p><br>
</div>
</div>
</div>
</section>
<section id="acceptedpapers">
<div class="content">
<p class="section_title">Accepted papers</p>
<div id="acceptedpapers-container">
<div class="acceptedpapers-container-item">
<ul class="acceptedpapers-container-item-list">
<li><b>Ontology-driven self-supervision for Adverse Childhood Experiences
identification using social media datasets</b> (<a href="https://www.scitepress.org/PublicationsDetail.aspx?ID=nz0IR5AMjiY=&t=1" class="acceptedpapers-container-item-link">paper</a>, <a href="https://github.com/hslu-abiz/sdaih22/blob/gh-pages/preprints/Ontology-driven%20self-supervision%20for%20Adverse%20Childhood%20Experiences%20identification%20using%20social%20media%20datasets%20(slides).pdf" class="acceptedpapers-container-item-link">slides</a>) - Jinge Wu, Rowena Smith and Honghan Wu <br></li>
<li><b>Towards reducing segmentation labeling costs for CMR imaging using
explainable AI</b> (<a href="https://www.scitepress.org/PublicationsDetail.aspx?ID=bjy/3kH1kTs=&t=1" class="acceptedpapers-container-item-link">paper</a>) - Alessa Stria and Asan Agibetov<br></li>
<li><b>Evaluation of the Synthetic Electronic Health Records</b> (<a href="https://www.scitepress.org/PublicationsDetail.aspx?ID=MVRu+0uThkc=&t=1" class="acceptedpapers-container-item-link">paper</a>, <a href="https://github.com/hslu-abiz/sdaih22/blob/gh-pages/preprints/Evaluation%20of%20the%20Synthetic%20Electronic%20Health%20Records%20%20(slides).pdf" class="acceptedpapers-container-item-link">slides</a>) - Emily Muller, Xu Zheng and Jer Hayes<br></li>
<li><b>Data Augmentation for Reliability and Fairness in Counselling Quality
Classification</b> (<a href="https://www.scitepress.org/PublicationsDetail.aspx?ID=ifHOadXiSUo=&t=1" class="acceptedpapers-container-item-link">paper</a>, <a href="https://github.com/hslu-abiz/sdaih22/blob/gh-pages/preprints/Data%20Augmentation%20for%20Reliability%20and%20Fairness%20in%20Counselling%20Quality%20Classification%20(slides).pdf" class="acceptedpapers-container-item-link">slides</a>) - Vivek Kumar, Simone Balloccu, Zixiu Wu,
Ehud Reiter, Rim Helaoui, Diego Reforgiato Recupero and Daniele Riboni<br></li>
<li><b>PT-MESS: a Problem-Transformation approach for Multi-Event
Survival analySis</b> (<a href="https://www.scitepress.org/PublicationsDetail.aspx?ID=7LxsW2VXtQE=&t=1" class="acceptedpapers-container-item-link">paper</a>, <a href="https://github.com/hslu-abiz/sdaih22/blob/gh-pages/preprints/PT-MESS%20-%20%20a%20Problem-Transformation%20approach%20for%20Multi-Event%20Survival%20analySis%20(slides).pdf" class="acceptedpapers-container-item-link">slides</a>) - Michela Venturini, Felipe Kenji Nakano and Celine Vens<br></li>
<li><b>How Much Data is Enough? Benchmarking Transfer Learning for Few
Shot ECG Image Classification</b> (<a href="https://www.scitepress.org/PublicationsDetail.aspx?ID=vpeoE0WIMjk=&t=1" class="acceptedpapers-container-item-link">paper</a>, <a href="https://github.com/hslu-abiz/sdaih22/blob/gh-pages/preprints/How%20Much%20Data%20is%20Enough%3F%20Benchmarking%20Transfer%20Learning%20for%20Few%20Shot%20ECG%20Image%20Classification%20(slides).pdf" class="acceptedpapers-container-item-link">slides</a>) - Sathvik Bhaskarpandit<br></li>
<li><b>Towards Reducing the Need for Annotations in Digital Dermatology with Self-Supervised Learning</b> (<a href="https://www.scitepress.org/PublicationsDetail.aspx?ID=rNZdHnDUChI=&t=1" class="acceptedpapers-container-item-link">paper</a>, <a href="https://github.com/hslu-abiz/sdaih22/blob/gh-pages/preprints/Towards%20Reducing%20the%20Need%20for%20Annotations%20in%20Digital%20Dermatology%20with%20Self-Supervised%20Learning%20(slides).pdf" class="acceptedpapers-container-item-link">slides</a>) - Fabian Gröger, Philippe Gottfrois, Ludovic Amruthalingam, Alvaro Gonzalez-Jimenez,
Simone Lionetti, Alexander A. Navarini and Marc Pouly<br></li>
<li><b>Eye-Tracking Dataset to Support the Research on Autism Spectrum Disorder</b> (<a href="https://www.scitepress.org/PublicationsDetail.aspx?ID=WvHahhUwnko=&t=1" class="acceptedpapers-container-item-link">paper</a>, <a href="https://github.com/hslu-abiz/sdaih22/blob/gh-pages/preprints/Eye-Tracking%20Dataset%20to%20Support%20the%20Research%20on%20Autism%20Spectrum%20Disorder%20(slides).pdf" class="acceptedpapers-container-item-link">slides</a>) - Federica Cilia, Romuald Carette, Mahmoud Elbattah, Jean-Luc Guérin and Gilles Dequen<br></li>
<li><b>Segmenting Overlapping Red Blood Cells With Classical Image Processing and Deep Learning</b> (<a href="https://www.scitepress.org/PublicationsDetail.aspx?ID=WH5lsaIcYJw=&t=1" class="acceptedpapers-container-item-link">paper</a>, <a href="https://github.com/hslu-abiz/sdaih22/blob/gh-pages/preprints/Segmenting%20Overlapping%20Red%20Blood%20Cells%20With%20Classical%20Image%20Processing%20and%20Deep%20Learning%20(slides).pdf" class="acceptedpapers-container-item-link">slides</a>) - Nils Brünggel, Pascal Vallotton and Patrick Conway<br></li>
<li><b>SANO: Score-based Anomaly Localization for Dermatology</b> (<a href="https://www.scitepress.org/PublicationsDetail.aspx?ID=8AuF/do+nSU=&t=1" class="acceptedpapers-container-item-link">paper</a>) - Alvaro Gonzalez-Jimenez, Simone Lionetti, Ludovic Amruthalingam, Philippe Gottfrois, Marc Pouly and Alexander Navarini<br></li>
</ul>
<br><br>
</p>
</div>
</div>
</div>
</section>
<section id="schedule">
<div class="content">
<p class="section_title">Schedule</p>
<p class="schedule-container-item-text">
<i>IJCAI/ECAI and SDAIH are fully in-person events and remote participation will be only considered in exceptional cases.</i>
</p>
<div id="schedule-container">
<div class="schedule-container-item">
<p class="schedule-container-item-subtitle">Scarce labels, 9:00 - 10:45</p>
<ul class="schedule-container-item-list">
<li>09:00 - 09:15 Welcome</li>
<li>09:15 - 09:35 Towards reducing segmentation labeling costs for CMR imaging using explainable AI (Alessa Stria)</li>
<li>09:35 - 09:55 Segmenting Overlapping Red Blood Cells With Classical Image Processing and Deep Learning (Nils Brünggel)</li>
<li>09:55 - 10:15 SANO: Score-based Anomaly Localization for Dermatology (Alvaro Gonzalez-Jimenez)</li>
<li>10:15 - 10:45 Discussion</li>
</ul>
<p class="schedule-container-item-subtitle">Coffee break <br> <br>
Self-Supervision, 11:15 - 12:15</p>
<ul class="schedule-container-item-list">
<li>11:15 - 11:35 Towards Reducing the Need for Annotations in Digital Dermatology with Self-Supervised Learning (Fabian Gröger)</li>
<li>11:35 - 11:55 Ontology-driven self-supervision for Adverse Childhood Experiences identification using social media datasets (Jinge Wu)</li>
<li>11:55 - 12:15 Discussion</li>
</ul>
<p class="schedule-container-item-subtitle">Lunch break <br><br>
Evaluation, 14:00 - 15:30</p>
<ul class="schedule-container-item-list">
<li>14:00 - 14:20 Data Augmentation for Reliability and Fairness in Counselling Quality Classification (Vivek Kumar)</li>
<li>14:20 - 14:40 Evaluation of the Synthetic Electronic Health Records (Jeremiah Hayes)</li>
<li>14:40 - 15:00 PT-MESS: A Problem-Transformation Approach for Multi-Event Survival Analysis (Felipe Kenji Nakano)</li>
<li>15:00 - 15:30 Discussion</li>
</ul>
<p class="schedule-container-item-subtitle">Coffee break <br><br>
Datasets and benchmarks, 16:00 - 17:00</p>
<ul class="schedule-container-item-list">
<li>16:00 - 16:20 Eye-Tracking Dataset to Support the Research on Autism Spectrum Disorder (Mahmoud Elbattah)</li>
<li>16:20 - 16:40 How Much Data is Enough? Benchmarking Transfer Learning for Few Shot ECG Image Classification (Sathvik Bhaskarpandit)</li>
<li>16:40 - 16:55 Discussion</li>
<li>16:55 - 17:00 Conclusion</li>
</ul>
<p class="schedule-container-item-text">
<img id="schedule-image" src="img/discussion_small.jpeg" alt="Discussion image">
</p>
</div>
</div>
</div>
</section>
<section id="committee">
<div class="content">
<p class="section_title">Organising committee</p>
<div id="committee-container">
<div class="committee-container-item">
<p class="committee-container-item-text">
<ul class="committee-container-item-list">
<li><a
href="https://www.hslu.ch/en/lucerne-university-of-applied-sciences-and-arts/about-us/people-finder/profile/?pid=4484"
class="namecommittee">Simone Lionetti</a> <br>Lucerne University of Applied Sciences and Arts <br><br>
</li>
<li><a
href="https://www.hslu.ch/en/lucerne-university-of-applied-sciences-and-arts/about-us/people-finder/profile/?pid=1494"
class="namecommittee">Marc Pouly</a> <br>Lucerne University of Applied Sciences and Arts
<br><br>
</li>
<li><a href="https://dbe.unibas.ch/en/persons/alexander-navarini/" class="namecommittee">Alexander
Navarini</a><br> University of Basel
<br><br>
</li>
<li><a
href="https://www.meduniwien.ac.at/web/forschung/researcher-profiles/researcher-profiles/index.php?id=688&res=philipp_tschandl"
class="namecommittee">Philipp Tschandl</a> <br>Medical University of Vienna <br>
</li>
</ul>
<br><br><br>
</p>
</div>
</div>
<p class="section_title">Program committee</p>
<div id="committee-container">
<div class="committee-container-item">
<p class="committee-container-item-text">
<ul class="committee-container-item-list">
<li><a
class="namecommittee">Catarina Barata</a> <br>University of Lisbon<br><br>
</li>
<li><a
class="namecommittee">Tim vor der Brück</a> <br>Lucerne University of Applied Sciences and Arts<br><br>
</li>
<li><a
class="namecommittee">Nicolas Deutschmann</a> <br>IBM Research<br><br>
</li>
<li><a
class="namecommittee">Koustav Ghosal</a> <br>Accenture<br><br>
</li>
<li><a class="namecommittee">Matthew Groh</a><br> MIT Media Lab <br><br>
</li>
<li><a
class="namecommittee">Fabian Ille</a> <br>Lucerne University of Applied Sciences and Arts<br><br>
</li>
<li><a
class="namecommittee">Thomas Koller</a> <br>Lucerne University of Applied Sciences and Arts<br><br>
</li>
<li><a class="namecommittee">Toni Mancini</a><br>"Sapienza" Università di Roma<br><br>
</li>
<li><a
class="namecommittee">Federico Mari</a> <br>"Foro Italico" Università di Roma <br><br>
</li>
<li><a
class="namecommittee">David Monaghan</a> <br>Trinity College Dublin<br><br>
</li>
<li><a class="namecommittee">Javier Montoya</a><br>Zurich University of Applied Sciences<br><br>
</li>
<li><a
class="namecommittee">Elif Ozkirimli</a> <br>Roche<br><br>
</li>
<li><a
class="namecommittee">Pushpak Pati</a> <br>IBM Research<br><br>
</li>
<li><a
class="namecommittee">Marianna Rapsomaniki</a> <br>IBM Research<br><br>
</li>
<li><a
class="namecommittee">Christoph Rinner</a> <br>Medical University of Vienna<br><br>
</li>
<li><a class="namecommittee">Veronica Rotemberg</a><br>Memorial Sloan Kettering Cancer Center<br><br>
</li>
<li><a
class="namecommittee">Robin Sandkühler</a> <br>University of Basel<br><br>
</li>
<li><a
class="namecommittee">Frank-Peter Schilling</a> <br>Zurich University of Applied Sciences<br><br>
</li>
<li><a
class="namecommittee">Philipp Schütz</a> <br>Lucerne University of Applied Sciences and Arts<br><br>
</li>
<li><a class="namecommittee">Andreas Streich</a><br>Lucerne University of Applied Sciences and Arts<br><br>
</li>
</ul>
<br><br><br>
</p>
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