Table Of ContentJana Fragemann · Jianning Li ·
Xiao Liu · Sotirios A. Tsaftaris ·
Jan Egger · Jens Kleesiek (Eds.)
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8 Medical Applications
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C with Disentanglements
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First MICCAI Workshop, MAD 2022
Held in Conjunction with MICCAI 2022
Singapore, September 22, 2022, Proceedings
Lecture Notes in Computer Science 13823
FoundingEditors
GerhardGoos
KarlsruheInstituteofTechnology,Karlsruhe,Germany
JurisHartmanis
CornellUniversity,Ithaca,NY,USA
EditorialBoardMembers
ElisaBertino
PurdueUniversity,WestLafayette,IN,USA
WenGao
PekingUniversity,Beijing,China
BernhardSteffen
TUDortmundUniversity,Dortmund,Germany
MotiYung
ColumbiaUniversity,NewYork,NY,USA
Moreinformationaboutthisseriesathttps://link.springer.com/bookseries/558
· · ·
Jana Fragemann Jianning Li Xiao Liu
· ·
Sotirios A. Tsaftaris Jan Egger
Jens Kleesiek (Eds.)
Medical Applications
with Disentanglements
First MICCAI Workshop, MAD 2022
Held in Conjunction with MICCAI 2022
Singapore, September 22, 2022
Proceedings
Editors
JanaFragemann JianningLi
EssenUniversityHospital GrazUniversityofTechnology
Essen,Germany Graz,Austria
XiaoLiu SotiriosA.Tsaftaris
UniversityofEdinburgh UniversityofEdinburgh
Edinburgh,UK Edinburgh,UK
JanEgger JensKleesiek
GrazUniversityofTechnology GermanCancerConsortium
Graz,Austria Essen,Germany
ISSN 0302-9743 ISSN 1611-3349 (electronic)
LectureNotesinComputerScience
ISBN 978-3-031-25045-3 ISBN 978-3-031-25046-0 (eBook)
https://doi.org/10.1007/978-3-031-25046-0
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Preface
Machine Learning applications have become very successful in recent years. In par-
ticular, deep learning (DL) has received a lot of attention and been included in many
challengesinthemedicalfield,includingtaskssuchassegmentation,classification,and
imagegeneration.However,DLlackssomeofthemostimportantfeaturesexpectedin
medicalapplication:trustworthinessandinterpretability.Mostneuralnetworksoperate
like black boxes and do not offer a way to understand the decision process. Shortcut
learningcanleadtowrongpredictionsorbadgeneralization.Especiallyinhealthcare,
these are huge problems as patients’ lives and well-being are affected. Thus, reliable,
trustworthy and understandable methods are needed. Therefore, looking more closely
intoaneuralnetworkcanhelp.Mostmodelsuseaso-calledlatentspacerepresentation,
adifferentrepresentationoftheinformationgiveninthedata.Givingthislatentspace
someinterpretableandcontrollablestructurehelpsovercometheblackboxcharacteristic
and highlights the features a network learns to make decisions. Therefore, this work-
shopaddressedthetopicofdisentanglement.ThiswasthefirsttimeweheldtheMedical
ApplicationswithDisentanglements(MAD)workshopattheMICCAIconference.
Ourreviewprocesswasdoubleblindandwehadtwotothreereviewersperpaper.We
acceptedeightpapers.Oneofthesepapersisashortone(sevenpages).Allothershave
atleasttenpages.Furthermore,weaddedanintroductorypapertooutlinethebeginning
ofthetopic.Theacceptedpaperscovergenerativeadversarialnetworks(GAN),varia-
tionalautoencoders(VAE)andnormalizing-flowarchitecturesaswellasawiderange
ofmedicalapplications,likebrainageprediction,skullreconstructionandunsupervised
pathologydisentanglement.WethankZhaodiDengfortheflyerdesignandKelseyL.
Pomykalaforproofreading.
September2022 JanaFragemann
JianningLi
XiaoLiu
SotiriosA.Tsaftaris
JanEgger
JensKleesiek
Organization
OrganizingCommittee
JanaFragemann InstituteforArtificialIntelligenceinMedicine,
Germany
JianningLi InstituteforArtificialIntelligenceinMedicine,
Germany
JanEgger InstituteforArtificialIntelligenceinMedicine,
Germany
JensKleesiek InstituteforArtificialIntelligenceinMedicine,
Germany
SotiriosA.Tsaftaris UniversityofEdinburgh,UK
XiaoLiu UniversityofEdinburgh,UK
ZhimingCui ShanghaiTechUniversity,China
VivekSharma HarvardUniversity,USA
ProgramCommittee
AlejandroF.Frangi UniversityofLeeds,UK
AnirbanMukhopadhyay TUDarmstadt,Germany
AsjaFischer RuhrUniversityBochum,Germany
ConstantinSeibold KarlsruheInstituteofTechnology,Germany
DanielRückert ImperialCollegeLondon,UK
FelixNensa InstituteforArtificialIntelligenceinMedicine,
Germany
JohannesKraus UniversityofDuisburg-Essen,Germany
JörgSchlötterer InstituteforArtificialIntelligenceinMedicine,
Germany
KaiUeltzhöffer EMBLHeidelberg,Germany
KeyvanFarahani NationalCancerInstitute,Rockville,MD,USA
KlausH.Maier-Hein GermanCancerResearchCenter,Germany
MichaelKamp InstituteforArtificialIntelligenceinMedicine,
Germany
NicolaRieke NVIDIA,Germany
NishantRavikumar UniversityofLeeds,UK
RobertSeifert UniversityHospitalEssen,Germany
SeppoVirtanen UniversityofLeeds,UK
Seyed-AhmadAhmadi NVIDIA,Germany
ShadiAlbarqouni UniversityHospitalBonn,Germany
viii Organization
VictorAlves UniversityofMinho,Portugal
AdditionalReviewers
FredericJonske InstituteforArtificialIntelligenceinMedicine,
Germany
JiahongOuyang StanfordUniversity,USA
Contents
Introduction
Applying Disentanglement in the Medical Domain: An Introduction
fortheMADWorkshop ................................................ 3
JanaFragemann, XiaoLiu, JianningLi, SotiriosA.Tsaftaris,
JanEgger,andJensKleesiek
GAN-BasedApproaches
HSIC-InfoGAN:LearningUnsupervisedDisentangledRepresentations
byMaximisingApproximatedMutualInformation ......................... 15
XiaoLiu, SpyridonThermos, PedroSanchez, AlisonQ.O’Neil,
andSotiriosA.Tsaftaris
Implicit Embeddings via GAN Inversion for High Resolution Chest
Radiographs .......................................................... 22
TobiasWeber,MichaelIngrisch,BerndBischl,andDavidRügamer
DisentangledRepresentationLearningforPrivacy-PreservingCase-Based
Explanations .......................................................... 33
HelenaMontenegro,WilsonSilva,andJaimeS.Cardoso
Autoencoder-BasedApproaches
Instance-Specific Augmentation of Brain MRIs with Variational
Autoencoders ......................................................... 49
JonMiddleton, MarkoBauer, JacobJohansen, MadsNielsen,
StefanSommer,andAkshayPai
Low-Rank and Sparse Metamorphic Autoencoders for Unsupervised
PathologyDisentanglement ............................................. 59
HristinaUzunova,HeinzHandels,andJanEhrhardt
Training β-VAE by Aggregating a Learned Gaussian Posterior
withaDecoupledDecoder .............................................. 70
JianningLi,JanaFragemann,Seyed-AhmadAhmadi,JensKleesiek,
andJanEgger
x Contents
Normalizing-Flow-BasedApproaches
DisentanglingFactorsofMorphologicalVariationinanInvertibleBrain
AgingModel ......................................................... 95
MatthiasWilms,PaulineMouches,JordanJ.Bannister,SönkeLangner,
andNilsD.Forkert
Comparision
A Study of Representational Properties of Unsupervised Anomaly
DetectioninBrainMRI ................................................ 111
AyantikaDas,ArunPalla,KeerthiRam,andMohanasankarSivaprakasam
AuthorIndex ......................................................... 127
