Predictive Intelligence in Medicine: Second International Workshop, PRIME 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings

190 Pages·2019·26.848 MB·English

by  Islem Rekik, Ehsan Adeli, Sang Hyun Park

Islem Rekik
Ehsan Adeli
Sang Hyun Park (Eds.)
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4
8 Predictive Intelligence
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C in Medicine
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Second International Workshop, PRIME 2019
Held in Conjunction with MICCAI 2019
Shenzhen, China, October 13, 2019
Proceedings
Lecture Notes in Computer Science 11843
Founding Editors
Gerhard Goos
Karlsruhe Institute of Technology, Karlsruhe, Germany
Juris Hartmanis
Cornell University, Ithaca, NY, USA
Editorial Board Members
Elisa Bertino
Purdue University, West Lafayette, IN, USA
Wen Gao
Peking University, Beijing, China
Bernhard Steffen
TU Dortmund University, Dortmund, Germany
Gerhard Woeginger
RWTH Aachen, Aachen, Germany
Moti Yung
Columbia University, New York, NY, USA
More information about this series at http://www.springer.com/series/7412
Islem Rekik Ehsan Adeli
(cid:129) (cid:129)
Sang Hyun Park (Eds.)
Predictive Intelligence
in Medicine
Second International Workshop, PRIME 2019
Held in Conjunction with MICCAI 2019
Shenzhen, China, October 13, 2019
Proceedings
123
Editors
Islem Rekik Ehsan Adeli
BASIRA StanfordUniversity
Istanbul TechnicalUniversity Stanford, CA,USA
Istanbul,Turkey
SangHyun Park
Daegu GyeongbukInstitute
of ScienceandTechnology
Daegu,Korea (Republic of)
ISSN 0302-9743 ISSN 1611-3349 (electronic)
Lecture Notesin Computer Science
ISBN 978-3-030-32280-9 ISBN978-3-030-32281-6 (eBook)
https://doi.org/10.1007/978-3-030-32281-6
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Preface
Itwouldconstituteastunningprogressinmedicineif,inafewyears,wecontributeto
engineering a ‘predictive intelligence’ able to predict missing clinical data with high
precision.Giventheoutburstofbigandcomplexmedicaldatawithmultiplemodalities
(e.g.,structuralmagneticresonanceimaging(MRI)andrestingfunctionMRI(rsfMRI))
and multiple acquisition time-points (e.g., longitudinal data), more intelligent
predictivemodelsareneededtoimprovediagnosisofawidespectrumofdiseasesand
disorders while leveraging minimal medical data. Basically, predictive intelligence in
medicine (PRIME workshop) primarily aims to diagnose in the earliest stage using
minimal clinically non-invasive data. For instance, PRIME would constitute a
breakthrough in early neurological disorder diagnosis as it would allow accurate early
diagnosis using multimodal MRI data (e.g., diffusion and functional MRIs) and
follow-up observations all predicted from only T1-weighted MRI acquired at baseline
time-point.
Existing computer-aided diagnosis methods can be divided into two main
categories: (1) analytical methods and (2) predictive methods. While analytical
methodsaimtoefficientlyanalyze,represent,andinterpretdata(staticorlongitudinal),
predictivemethodsleveragethedatacurrentlyavailabletopredictobservationsatlater
time-points(i.e.,forecastingthefuture)orpredictingobservationsatearliertime-points
(i.e.,predictingthepastformissingdatacompletion).Forinstance,amethodthatonly
focusesonclassifyingpatientswithmildcognitiveimpairment(MCI)andpatientswith
Alzheimer’s disease (AD) is an analytical method, while a method which predicts if a
subject diagnosed with MCI will remain stable or convert to AD over time is a
predictive method. Similar examples can be established for various neurodegenerative
or neuropsychiatric disorders, degenerative arthritis, or in cancer studies, in which the
disease or disorder develops over time.
Following the success of the first edition of PRIME-MICCAI in 2018, the second
editionofPRIME2019aimedtodrivethefieldof‘high-precisionpredictivemedicine,’
where late medical observations are predicted with high precision, while providing
explanation via machine and deep learning, and statistically, mathematically, or
physically-based models of healthy, disordered development, and aging. Despite the
terrific progress that analytical methods have made in the last 20 years in medical
image segmentation, registration, or other related applications, efficient predictive
intelligent models and methods are somewhat lagging behind. As such predictive
intelligence develops and improves (and this is likely to do so exponentially in the
coming years), this will have far-reaching consequences for the development of new
treatment procedures and novel technologies. These predictive models will begin to
shedlightononeofthemostcomplexhealthcareandmedicalchallengeswehaveever
encountered, and, in doing so, change our basic understanding of who we are.
vi Preface
What Are the Key Challenges We Aim to Address?
The main aim of PRIME-MICCAI is to propel the advent of predictive models in a
broadsense,withapplicationtomedicaldata.Particularly,theworkshopaccepted8-to
12-page papers describing newcutting-edgepredictive modelsandmethods thatsolve
challenging problems in the medical field. We hope that the PRIME workshop
becomes a nest for high-precision predictive medicine – one that is set to transform
multiplefieldsofhealthcaretechnologiesinunprecedentedways.Topicsofinterestfor
the workshop included but were not limited to predictive methods dedicated to the
following topics:
– Modeling and predicting disease development or evolution from a limited number
of observations
– Computer-aided prognostic methods (e.g., for brain diseases, prostate cancer,
cervical cancer, dementia, acute disease, neurodevelopmental disorders)
– Forecasting disease or cancer progression over time
– Predicting low-dimensional data (e.g., behavioral scores, clinical outcome, age,
gender)
– Predicting the evolution or development of high-dimensional data (e.g., shapes,
graphs, images, patches, abstract features, learned features)
– Predicting high-resolution data from low-resolution data
– Prediction methods using 2D, 2D+t, 3D, 3D+t, ND, and ND+t data
– Predicting data of one image modality from a different modality (e.g., data
synthesis)
– Predicting lesion evolution
– Predicting missing data (e.g., data imputation or data completion problems)
– Predicting clinical outcome from medical data (genomic, imaging data, etc)
Key Highlights
This workshop mediated ideas from both machine learning and mathematical,
statistical,andphysicalmodelingresearchdirectionsinthehopeofprovidingadeeper
understanding of thefoundations ofpredictive intelligence developedfor medicine, as
well as to where we currently stand and what we aspire to achieve through this field.
PRIME-MICCAI 2019 featured a single-track workshop with keynote speakers with
deepexpertiseinhigh-precisionpredictivemedicine usingmachine learning andother
modelingapproacheswhicharebelievedtostandatopposingdirections.Ourworkshop
also included technical paper presentations, poster sessions, and demonstrations.
Eventually,thishelpssteerawidespectrumofMICCAIpublicationsfrombeing‘only
analytical’ to being ‘jointly analytical and predictive.’
We received a total of 18 submissions. All papers underwent a rigorous
double-blinded review process by at least 2 members (mostly 3 members) of the
Program Committee composed of 26 well-known research experts in the field.
Preface vii
Theselectionofthepaperswasbasedontechnicalmerit,significanceofresults,and
relevance and clarity of presentation. Based on the reviewing scores and critiques, all
PRIME2019submissionsscoredhighbyreviewers,i.e.,allhadanaveragescoreofat
least above the acceptance threshold. Hence all 18 submissions were approved for
publication in the present proceedings.
August 2019 Islem Rekik
Ehsan Adeli
Sang Hyun Park
Organization
Chairs
Islem Rekik Istanbul Technical University, Turkey
Ehsan Adeli Stanford University, USA
Sang Hyun Park DGIST, South Korea
Program Committee
Amir Alansary Imperial College London, UK
Changqing Zhang Tianjin University, China
Dong Nie University of North Carolina, USA
Duygu Sarikaya University of Rennes 1, France
Gerard Sanroma Pompeu Fabra University, Spain
Guorong Wu University of North Carolina, USA
Heung-Il Suk Korea University, South Korea
Ilwoo Lyu Vanderbilt University, USA
Ipek Oguz University of Pennsylvania, USA
Jaeil Kim Kyungpook National University, South Korea
Le Lu PAII Inc., USA
Lichi Zhang Shanghai Jiao Tong University, China
Mara Valds Hernndez University of Edinburgh, UK
Minjeong Kim University of North Carolina at Greensboro, USA
Nesrine Bnouni National Engineering School of Sousse (ENISo),
Tunisia
Pew-Thian Yap University of North Carolina, USA
Qian Wang Shanghai Jiao Tong University, China
Qingyu Zhao Stanford University, USA
Seyed Mostafa Kia Donders Institute, The Netherlands
Stefanie Demirci Technische Universität München, Germany
Sophia Bano University College London, UK
Ulas Bagci University of Central Florida, USA
Xiaohuan Cao United Imaging Intelligence, China
Yu Zhang Stanford University, USA
Yue Gao Tsinghua University, China
Ziga Spiclin University of Ljubljana, Slovenia
Contents
TADPOLE Challenge: Accurate Alzheimer’s Disease Prediction Through
Crowdsourced Forecasting of Future Data . . . . . . . . . . . . . . . . . . . . . . . . . 1
Răzvan V. Marinescu, Neil P. Oxtoby, Alexandra L. Young,
Esther E.Bron,Arthur W.Toga,Michael W.Weiner, FrederikBarkhof,
Nick C. Fox, Polina Golland, Stefan Klein, and Daniel C. Alexander
Inter-fractionalRespiratoryMotionModellingfromAbdominalUltrasound:
A Feasibility Study. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
Alina Giger, Christoph Jud, Damien Nguyen, Miriam Krieger,
Ye Zhang, Antony J. Lomax, Oliver Bieri, Rares Salomir,
and Philippe C. Cattin
Adaptive Neuro-Fuzzy Inference System-Based Chaotic Swarm
Intelligence Hybrid Model for Recognition of Mild Cognitive
Impairment from Resting-State fMRI. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
Ahmed M. Anter and Zhiguo Zhang
Deep Learning via Fused Bidirectional Attention Stacked Long
Short-Term Memory for Obsessive-Compulsive Disorder Diagnosis
and Risk Screening. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
Chiyu Feng, Lili Jin, Chuangyong Xu, Peng Yang, Tianfu Wang,
Baiying Lei, and Ziwen Peng
Modeling Disease Progression in Retinal OCTs with Longitudinal
Self-supervised Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
Antoine Rivail, Ursula Schmidt-Erfurth, Wolf-Dieter Vogel,
Sebastian M. Waldstein, Sophie Riedl, Christoph Grechenig,
Zhichao Wu, and Hrvoje Bogunovic
Predicting Response to the Antidepressant Bupropion Using
Pretreatment fMRI. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
Kevin P. Nguyen, Cherise Chin Fatt, Alex Treacher, Cooper Mellema,
Madhukar H. Trivedi, and Albert Montillo
Progressive Infant Brain Connectivity Evolution Prediction
from Neonatal MRI Using Bidirectionally Supervised Sample Selection. . . . . 63
Olfa Ghribi, Gang Li, Weili Lin, Dinggang Shen, and Islem Rekik