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001481323 001__ 1481323
001481323 003__ OCoLC
001481323 005__ 20231031003332.0
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001481323 020__ $$a9783031439872$$q(electronic bk.)
001481323 020__ $$a3031439872$$q(electronic bk.)
001481323 020__ $$z9783031439865$$q(print)
001481323 0247_ $$a10.1007/978-3-031-43987-2$$2doi
001481323 035__ $$aSP(OCoLC)1401635640
001481323 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dEBLCP
001481323 049__ $$aISEA
001481323 050_4 $$aRC78.7.D53$$bI58 2023eb
001481323 08204 $$a616.07/54$$223/eng/20231003
001481323 1112_ $$aInternational Conference on Medical Image Computing and Computer-Assisted Intervention$$n(26th :$$d2023 :$$cVancouver, B.C. ; Online)
001481323 24510 $$aMedical image computing and computer assisted intervention -- MICCAI 2023 :$$b26th International Conference, Vancouver, BC, Canada, October 8-12, 2023, Proceedings.$$nPart VI /$$cHayit Greenspan, Anant Madabhushi, Parvin Mousavi, Septimiu Salcudean, James Duncan, Tanveer Syeda-Mahmood, Russell Taylor, editors.
001481323 2463_ $$aMICCAI 2023
001481323 264_1 $$aCham, Switzerland :$$bSpringer,$$c2023.
001481323 300__ $$a1 online resource (xxxix, 814 pages) :$$billustrations (some color).
001481323 336__ $$atext$$btxt$$2rdacontent
001481323 337__ $$acomputer$$bc$$2rdamedia
001481323 338__ $$aonline resource$$bcr$$2rdacarrier
001481323 4901_ $$aLecture notes in computer science,$$x1611-3349 ;$$v14225
001481323 500__ $$aIncludes author index.
001481323 5050_ $$aIntro -- Preface -- Organization -- Contents - Part VI -- Computer-Aided Diagnosis II -- Mining Negative Temporal Contexts for False Positive Suppression in Real-Time Ultrasound Lesion Detection -- 1 Introduction -- 2 Related Works -- 3 Method -- 3.1 Basic Real-Time Detector -- 3.2 Negative Temporal Context Aggregation -- 3.3 UltraDet for Real-Time Lesion Detection -- 4 Experiments -- 4.1 Dateset -- 4.2 Evaluation Metrics -- 4.3 Implementation Details -- 4.4 Main Results -- 4.5 Ablation Study -- 5 Conclusion -- References
001481323 5058_ $$aCombat Long-Tails in Medical Classification with Relation-Aware Consistency and Virtual Features Compensation -- 1 Introduction -- 2 Methodology -- 2.1 Multi-view Relation-Aware Consistency -- 2.2 Virtual Features Compensation -- 3 Experiments -- 3.1 Datasets -- 3.2 Implementation Details -- 3.3 Comparison on ISIC-2019-LT Dataset -- 3.4 Comparison on ISIC-Archive-LT Dataset -- 4 Conclusion -- References -- Towards Novel Class Discovery: A Study in Novel Skin Lesions Clustering -- 1 Introduction -- 2 Methodology -- 3 Experiments -- 4 Conclusion -- References
001481323 5058_ $$aJoint Segmentation and Sub-pixel Localization in Structured Light Laryngoscopy -- 1 Introduction -- 2 Related Work -- 3 Method -- 4 Evaluation -- 5 Conclusion -- References -- A Style Transfer-Based Augmentation Framework for Improving Segmentation and Classification Performance Across Different Sources in Ultrasound Images -- 1 Introduction -- 2 Methods -- 2.1 Mixed Style Augmentation (MixStyleAug) -- 2.2 Network Architecture and Feature Augmentation (FeatAug) -- 2.3 Mask-Based Style Augmentation (MaskAug) -- 2.4 Loss Function and Implementation Details -- 3 Experimental Results and Discussion
001481323 5058_ $$a4 Conclusion -- References -- HC-Net: Hybrid Classification Network for Automatic Periodontal Disease Diagnosis -- 1 Introduction -- 2 Method -- 2.1 Task Formulation and Method Overview -- 2.2 Tooth-Level Classification -- 2.3 Patient-Level Classification -- 2.4 Learnable Adaptive Noisy-OR Gate -- 3 Experiments -- 3.1 Dataset and Evaluation Metrics -- 3.2 Comparison with Other Methods -- 3.3 Ablation Studies -- 3.4 Implementation Details -- 4 Conclusion -- References -- SegNetr: Rethinking the Local-Global Interactions and Skip Connections in U-Shaped Networks -- 1 Introduction -- 2 Method
001481323 5058_ $$a2.1 SegNetr Block -- 2.2 Information Retention Skip Connection -- 3 Experiments and Discussion -- 3.1 Comparison with State-of-the-Arts -- 3.2 Ablation Study -- 4 Conclusion -- References -- Gradient and Feature Conformity-Steered Medical Image Classification with Noisy Labels -- 1 Introduction -- 2 Method -- 2.1 Gradient and Feature Conformity-Based Clean Data Selection -- 2.2 Sample Reliability-Based Mixup (SRMix) -- 3 Experiments -- 3.1 Datasets and Implementation Details -- 3.2 Experimental Results -- 4 Conclusion -- References
001481323 506__ $$aAccess limited to authorized users.
001481323 520__ $$aThe ten-volume set LNCS 14220, 14221, 14222, 14223, 14224, 14225, 14226, 14227, 14228, and 14229 constitutes the refereed proceedings of the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023, which was held in Vancouver, Canada, in October 2023. The 730 revised full papers presented were carefully reviewed and selected from a total of 2250 submissions. The papers are organized in the following topical sections: Part I: Machine learning with limited supervision and machine learning - transfer learning; Part II: Machine learning -- learning strategies; machine learning -- explainability, bias, and uncertainty; Part III: Machine learning -- explainability, bias and uncertainty; image segmentation; Part IV: Image segmentation; Part V: Computer-aided diagnosis; Part VI: Computer-aided diagnosis; computational pathology; Part VII: Clinical applications -- abdomen; clinical applications -- breast; clinical applications -- cardiac; clinical applications -- dermatology; clinical applications -- fetal imaging; clinical applications -- lung; clinical applications -- musculoskeletal; clinical applications -- oncology; clinical applications -- ophthalmology; clinical applications -- vascular; Part VIII: Clinical applications -- neuroimaging; microscopy; Part IX: Image-guided intervention, surgical planning, and data science; Part X: Image reconstruction and image registration.
001481323 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed October 3, 2023).
001481323 650_0 $$aDiagnostic imaging$$xData processing$$vCongresses.$$0(DLC)sh2007006024
001481323 655_0 $$aElectronic books.
001481323 7001_ $$aGreenspan, Hayit,$$eeditor.
001481323 7001_ $$aMadabhushi, Anant,$$eeditor.$$1https://orcid.org/0000-0002-5741-0399
001481323 7001_ $$aMousavi, Parvin,$$eeditor.
001481323 7001_ $$aSalcudean, Septimiu Edmund,$$eeditor.$$1https://orcid.org/0000-0001-8826-8025
001481323 7001_ $$aDuncan, James,$$d1951-$$eeditor.$$1https://orcid.org/0000-0002-5167-9856$$0(OCoLC)oca00745149
001481323 7001_ $$aSyeda-Mahmood, Tanveer,$$eeditor.$$1https://orcid.org/0000-0003-0059-3208
001481323 7001_ $$aTaylor, Russell,$$eeditor.$$0(orcid)0000-0001-6272-1100$$1https://orcid.org/0000-0001-6272-1100
001481323 830_0 $$aLecture notes in computer science ;$$v14225.$$x1611-3349
001481323 852__ $$bebk
001481323 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-43987-2$$zOnline Access$$91397441.1
001481323 909CO $$ooai:library.usi.edu:1481323$$pGLOBAL_SET
001481323 980__ $$aBIB
001481323 980__ $$aEBOOK
001481323 982__ $$aEbook
001481323 983__ $$aOnline
001481323 994__ $$a92$$bISE