Medical Applications with Disentanglements: First MICCAI Workshop, MAD 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings PDF
Preview Medical Applications with Disentanglements: First MICCAI Workshop, MAD 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings
Jana Fragemann · Jianning Li · Xiao Liu · Sotirios A. Tsaftaris · Jan Egger · Jens Kleesiek (Eds.) 3 2 8 Medical Applications 3 1 S C with Disentanglements N L 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 ©TheEditor(s)(ifapplicable)andTheAuthor(s),underexclusivelicense toSpringerNatureSwitzerlandAG2023 Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpartofthe material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilarmethodologynow knownorhereafterdeveloped. Theuseofgeneraldescriptivenames,registerednames,trademarks,servicemarks,etc.inthispublication doesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfromtherelevant protectivelawsandregulationsandthereforefreeforgeneraluse. Thepublisher,theauthors,andtheeditorsaresafetoassumethattheadviceandinformationinthisbookare believedtobetrueandaccurateatthedateofpublication.Neitherthepublishernortheauthorsortheeditors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissionsthatmayhavebeenmade.Thepublisherremainsneutralwithregardtojurisdictionalclaimsin publishedmapsandinstitutionalaffiliations. ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSwitzerlandAG Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland 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
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