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001440715 019__ $$a1280195122$$a1280274650$$a1281958497$$a1287778317$$a1292518790
001440715 020__ $$a9783030898472$$q(electronic bk.)
001440715 020__ $$a3030898474$$q(electronic bk.)
001440715 020__ $$z3030898466
001440715 020__ $$z9783030898465
001440715 0247_ $$a10.1007/978-3-030-89847-2$$2doi
001440715 035__ $$aSP(OCoLC)1281955694
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001440715 049__ $$aISEA
001440715 050_4 $$aRC78.7.D53
001440715 08204 $$a616.07/57$$223
001440715 1112_ $$aML-CDS (Workshop)$$n(11th :$$d2021 :$$cOnline)
001440715 24510 $$aMultimodal learning for clinical decision support :$$b11th International Workshop, ML-CDS 2021, held in conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings /$$cTanveer Syeda-Mahmood, Xiang Li, Anant Madabhushi, Hayit Greenspan, Quanzheng Li, Richard Leahy, Bin Dong, Hongzhi Wang (eds.).
001440715 2463_ $$aML-CDS 2021
001440715 260__ $$aCham :$$bSpringer,$$c2021.
001440715 300__ $$a1 online resource (125 pages)
001440715 336__ $$atext$$btxt$$2rdacontent
001440715 337__ $$acomputer$$bc$$2rdamedia
001440715 338__ $$aonline resource$$bcr$$2rdacarrier
001440715 347__ $$atext file
001440715 347__ $$bPDF
001440715 4901_ $$aLecture notes in computer science ;$$v13050
001440715 4901_ $$aLNCS sublibrary, SL 6, Image processing, computer vision, pattern recognition, and graphics
001440715 500__ $$a"Which was held virtually at MICCAI 2021"--Preface
001440715 500__ $$a1 Introduction.
001440715 500__ $$aIncludes author index.
001440715 504__ $$aReferences-A Federated Multigraph Integration Approach for Connectional Brain Template Learning-1 Introduction-2 Proposed Method-3 Results and Discussion-4 Conclusion-References-SAMA: Spatially-Aware Multimodal Network with Attention For Early Lung Cancer Diagnosis-1 Introduction-2 Method-2.1 SAMA Module-3 Experimental Setup-3.1 Dataset-3.2 Implementation Details-4 Results-4.1 Control Experiments-5 Conclusions-References-Fully Automatic Head and Neck Cancer Prognosis Prediction in PET/CT-1 Introduction-2 Methods-2.1 Datasets.
001440715 5050_ $$aIntro -- Preface -- Organization -- Contents -- From Picoscale Pathology to Decascale Disease: Image Registration with a Scattering Transform and Varifolds for Manipulating Multiscale Data -- 1 Introduction -- 2 Methods -- 2.1 Image Acquisition and Processing -- 2.2 Algorithm for Multimodal Registration with Damaged Tissue -- 2.3 Scattering Transform for Retaining High Resolution Texture -- 2.4 Varifold Measures for Modeling and Crossing Multiple Scales -- 3 Results -- 4 Discussion -- References -- Multi-scale Hybrid Transformer Networks: Application to Prostate Disease Classification
001440715 5058_ $$a1 Introduction -- 1.1 Contribution -- 2 Methods -- 2.1 Model Comparison -- 3 Experiments and Results -- 3.1 Dataset -- 3.2 Pre-processing and Augmentation -- 3.3 Training -- 3.4 Results -- 4 Conclusion -- References -- Predicting Treatment Response in Prostate Cancer Patients Based on Multimodal PET/CT for Clinical Decision Support -- 1 Introduction -- 2 Methods -- 2.1 Pipeline Overview -- 2.2 Dataset and Ground Truth Annotation -- 2.3 Automated Segmentation -- 2.4 Therapy Response Prediction -- 3 Results -- 3.1 Segmentation -- 3.2 Therapy Response Prediction -- 4 Discussion -- 5 Conclusion
001440715 5058_ $$a2.2 UNet Architecture and Training -- 2.3 Radiomics Workflow -- 3 Results -- 3.1 GTVt Segmentation -- 3.2 Prognosis Prediction -- 4 Discussion and Conclusions -- References -- Feature Selection for Privileged Modalities in Disease Classification -- 1 Introduction -- 2 Background -- 2.1 Learning Using Privileged Information -- 2.2 Mutual Information Feature Selection -- 3 Method -- 4 Experiments -- 4.1 Compared LUPI Models -- 4.2 Datasets -- 5 Results -- 5.1 Parkinson's Dataset -- 5.2 TMJ Osteoarthritis Dataset -- 6 Conclusions -- References
001440715 5058_ $$aMerging and Annotating Teeth and Roots from Automated Segmentation of Multimodal Images -- 1 Introduction -- 2 Materials -- 3 Related Work -- 3.1 Root Canal Segmentation Algorithm -- 3.2 3D Shape Analysis for Segmentation and Classification -- 4 Methods -- 4.1 Root Canal Segmentation Algorithm -- 4.2 Dental Model Segmentation Algorithm -- 4.3 Universal Labeling and Merging Algorithm -- 5 Results -- 5.1 Root Canal Segmentation -- 5.2 Universal Labeling and Merging Algorithm -- 6 Conclusion -- References -- Structure and Feature Based Graph U-Net for Early Alzheimer's Disease Prediction
001440715 506__ $$aAccess limited to authorized users.
001440715 520__ $$aThis book constitutes the refereed joint proceedings of the 11th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2021, held in conjunction with the 24th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2021, in Strasbourg, France, in October 2021. The workshop was held virtually due to the COVID-19 pandemic. The 10 full papers presented at ML-CDS 2021 were carefully reviewed and selected from numerous submissions. The ML-CDS papers discuss machine learning on multimodal data sets for clinical decision support and treatment planning.
001440715 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed November 3, 2021).
001440715 650_0 $$aDiagnostic imaging$$xData processing$$vCongresses.
001440715 650_0 $$aComputer-assisted surgery$$vCongresses.
001440715 650_6 $$aImagerie pour le diagnostic$$xInformatique$$vCongrès.
001440715 650_6 $$aChirurgie assistée par ordinateur$$vCongrès.
001440715 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772
001440715 655_7 $$aConference papers and proceedings.$$2lcgft
001440715 655_7 $$aActes de congrès.$$2rvmgf
001440715 655_0 $$aElectronic books.
001440715 7001_ $$aSyeda-Mahmood, Tanveer.
001440715 7001_ $$aLi, Xiang.
001440715 7001_ $$aMadabhushi, Anant.
001440715 7001_ $$aGreenspan, Hayit.
001440715 7001_ $$aLi, Quanzheng.
001440715 7001_ $$aLeahy, Richard M.
001440715 7001_ $$aDong, Bin$$c(Professor)
001440715 7001_ $$aWang, Hongzhi.
001440715 7112_ $$aInternational Conference on Medical Image Computing and Computer-Assisted Intervention$$n(24th :$$d2021 :$$cOnline)
001440715 77608 $$iPrint version:$$aSyeda-Mahmood, Tanveer.$$tMultimodal Learning for Clinical Decision Support.$$dCham : Springer International Publishing AG, ©2021$$z9783030898465
001440715 830_0 $$aLecture notes in computer science ;$$v13050.
001440715 830_0 $$aLNCS sublibrary.$$nSL 6,$$pImage processing, computer vision, pattern recognition, and graphics.
001440715 852__ $$bebk
001440715 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-89847-2$$zOnline Access$$91397441.1
001440715 909CO $$ooai:library.usi.edu:1440715$$pGLOBAL_SET
001440715 980__ $$aBIB
001440715 980__ $$aEBOOK
001440715 982__ $$aEbook
001440715 983__ $$aOnline
001440715 994__ $$a92$$bISE