Resource-Efficient Medical Image Analysis: First MICCAI Workshop, REMIA 2022, Singapore, September 22, 2022, Proceedings PDF
Preview Resource-Efficient Medical Image Analysis: First MICCAI Workshop, REMIA 2022, Singapore, September 22, 2022, Proceedings
Xinxing Xu · Xiaomeng Li · Dwarikanath Mahapatra · Li Cheng · Caroline Petitjean · Huazhu Fu (Eds.) 3 4 5 Resource-Efficient 3 1 S C Medical Image Analysis N L First MICCAI Workshop, REMIA 2022 Singapore, September 22, 2022 Proceedings Lecture Notes in Computer Science 13543 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 · · Xinxing Xu Xiaomeng Li · · Dwarikanath Mahapatra Li Cheng · Caroline Petitjean Huazhu Fu (Eds.) Resource-Efficient Medical Image Analysis First MICCAI Workshop, REMIA 2022 Singapore, September 22, 2022 Proceedings Editors XinxingXu XiaomengLi InstituteofHighPerformanceComputing HongKongUniversityofScience Singapore,Singapore andTechnology HongKong,China DwarikanathMahapatra InceptionInstituteofArtificialIntelligence LiCheng AbuDhabi,UnitedArabEmirates UniversityofAlberta Edmonton,AB,Canada CarolinePetitjean UniversityofRouen HuazhuFu Rouen,France InstituteofHighPerformanceComputing Singapore,Singapore ISSN 0302-9743 ISSN 1611-3349 (electronic) LectureNotesinComputerScience ISBN 978-3-031-16875-8 ISBN 978-3-031-16876-5 (eBook) https://doi.org/10.1007/978-3-031-16876-5 ©TheEditor(s)(ifapplicable)andTheAuthor(s),underexclusivelicense toSpringerNatureSwitzerlandAG2022 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. 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ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSwitzerlandAG Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland Preface The 1st International Workshop on Resource-Efficient Medical Image Analysis (REMIA 2022) was held on September 22, 2022, in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2022). This will be the first MICCAI conference hosted in Southeast Asia. Due to COVID-19, this year it was a hybrid (virtual + in-person) conference. Deep learning methods have shown remarkable success in many medical imaging tasks over the past few years. However, it remains a challenge that current deep learning models are usually data-hungry, requiring massive amounts of high-quality annotated data for high performance. Firstly, collecting large scale medical imaging datasets is expensive and time-consuming, and the regulatory and governance aspects also raise additional challenges for large scale datasets for healthcare applications. Secondly, the data annotations are even more of a challenge as experienced and knowledgeable clinicians are required to achieve high-quality annotations. The annotation becomes more challenging when it comes to the segmentation tasks. It is infeasibletoadaptdata-hungrydeeplearningmodelstoachievevariousmedicaltasks within a low-resource situation. However, the vanilla deep learning models usually have the limited ability of learning from limited training samples. Consequently, to enable efficient and practical deep learning models for medical imaging, there is a need for research methods that can handle limited training data, limited labels, and limitedhardwareconstraintswhendeployingthemodel. The workshop focused on the issues for practical applications of the most common medical imaging systems with data, label and hardware limitations. It brought together AI scientists, clinicians, and students from different disciplines and areas for medical image analysis to discuss the related advancements in the field. A total of 19 full-length papers were submitted to the workshop in response to the call for papers. All submissions were double-blind peer-reviewed by at least three members of the Program Committee. Paper selection was based on methodological innovation, technical merit, results, validation, and application potential.Finally,13paperswereacceptedattheworkshopandchosentobeincluded inthisSpringerLNCSvolume. We are grateful to the Program Committee for reviewing the submitted papers and giving constructive comments and critiques, to the authors for submitting vi Preface high-qualitypapers,tothepresentersforexcellentpresentations,andtoalltheREMIA 2022attendeesfromallaroundtheworld. August2022 XinxingXu XiaomengLi DwarikanathMahapatra LiCheng CarolinePetitjean HuazhuFu Organization WorkshopChairs XinxingXu IHPC,A*STAR,Singapore XiaomengLi HongKongUniversityofScienceand Technology,HongKong,China DwarikanathMahapatra InceptionInstituteofArtificialIntelligence, AbuDhabi,UAE LiCheng ECE,UniversityofAlberta,Canada CarolinePetitjean LITIS,UniversityofRouen,France HuazhuFu InstituteofHighPerformanceComputing, A*STAR,Singapore LocalOrganizers RickGohSiowMong IHPC,A*STAR,Singapore YongLiu IHPC,A*STAR,Singapore ProgramCommittee BehzadBozorgtabar EPFL,Switzerland ÉlodiePuybareau EPITA,France ErjianGuo UniversityofSydney,Australia HeZhao BeijingInstituteofTechnology,China HengLi SouthernUniversityofScienceandTechnology, China JiaweiDu IHPC,A*STAR,Singapore JinkuiHao NingboInstituteofIndustrialTechnology,CAS, China KangZhou ShanghaiTechUniversity,China KeZou SichuanUniversity,China MengWang IHPC,A*STAR,Singapore OlfaBenAhmed UniversityofPoitiers,France PushpakPati IBMResearchZurich,Switzerland SarahLeclerc UniversityofBurgundy,France ShaohuaLi IHPC,A*STAR,Singapore ShihaoZhang NationalUniversityofSingapore,Singapore TaoZhou NanjingUniversityofScienceandTechnology, China viii Organization XiaofengLei IHPC,A*STAR,Singapore YanHu SouthernUniversityofScienceandTechnology, China YanmiaoBai NingboInstituteofIndustrialTechnology,CAS, China YanyuXu IHPC,A*STAR,Singapore YimingQian IHPC,A*STAR,Singapore YinglinZhang SouthernUniversityofScienceandTechnology, China YumingJiang NanyangTechnologicalUniversity,Singapore Contents Multi-taskSemi-supervisedLearningforVascularNetworkSegmentation andRenalCellCarcinomaClassification .................................. 1 RudanXiao,DamienAmbrosetti,andXavierDescombes Self-supervisedAntigenDetectionArtificialIntelligence(SANDI) ........... 12 HanyunZhang,KhalidAbdulJabbar,TamiGrunewald,AyseAkarca, YemanHagos, CatherineLecat, DominicPate, LydiaLee, ManuelRodriguez-Justo, KweeYong, JonathanLedermann, JohnLeQuesne,TeresaMarafioti,andYinyinYuan RadTex:LearningEfficientRadiographRepresentationsfromTextReports .... 22 KeeganQuigley, MiriamCha, RuizhiLiao, GeetickaChauhan, StevenHorng,SethBerkowitz,andPolinaGolland SingleDomainGeneralizationviaSpontaneous AmplitudeSpectrum Diversification ........................................................ 32 YuexiangLi,NanjunHe,andYawenHuang Triple-ViewFeatureLearningforMedicalImageSegmentation .............. 42 ZiyangWangandIrinaVoiculescu Classificationof4DfMRIImagesUsingML,FocusingonComputational andMemoryUtilizationEfficiency ....................................... 55 NazaninBeheshtiandLennartJohnsson AnEfficientDefendingMechanismAgainstImageAttackingonMedical ImageSegmentationModels ............................................ 65 LinhD.Le,HuazhuFu,XinxingXu,YongLiu,YanyuXu,JiaweiDu, JoeyT.Zhou,andRickGoh LeverageSupervisedandSelf-supervisedPretrainModelsforPathological SurvivalAnalysisviaaSimpleandLow-costJointRepresentationTuning ..... 75 QuanLiu, CanCui, RuiningDeng, ZuhayrAsad, TianyuanYao, ZheyuZhu,andYuankaiHuo PathologicalImageContrastiveSelf-supervisedLearning ................... 85 WenkangQin,ShanJiang,andLinLuo Investigation of Training Multiple Instance Learning Networks withInstanceSampling ................................................. 95 AliasgharTarkhan,TrungKienNguyen,NoahSimon,andJianDai
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