Table Of ContentXinxing 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.
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
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
