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001482326 001__ 1482326
001482326 003__ OCoLC
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001482326 008__ 231012s2023\\\\sz\a\\\\o\\\\\101\0\eng\d
001482326 020__ $$a9783031449178$$q(electronic bk.)
001482326 020__ $$a3031449177$$q(electronic bk.)
001482326 020__ $$z9783031449185
001482326 0247_ $$a10.1007/978-3-031-44917-8$$2doi
001482326 035__ $$aSP(OCoLC)1402285499
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001482326 08204 $$a616.07/54$$223/eng/20231012
001482326 1112_ $$aMILLanD (Workshop) :$$n(2nd :$$d2023 :$$cVancouver, B.C.).
001482326 24510 $$aMedical image learning with limited and noisy data :$$bsecond international workshop, MILLanD 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, proceedings /$$cZhiyun Xue, Sameer Antani, Ghada Zamzmi, Feng Yang, Sivaramakrishnan Rajaraman, Sharon Xiaolei Huang, Marius George Linguraru, Zhaohui Liang, editors.
001482326 24630 $$aMILLanD 2023
001482326 264_1 $$aCham :$$bSpringer,$$c[2023]
001482326 264_4 $$c©2023
001482326 300__ $$a1 online resource (xi, 270 pages) :$$billustrations (chiefly color).
001482326 336__ $$atext$$btxt$$2rdacontent
001482326 337__ $$acomputer$$bc$$2rdamedia
001482326 338__ $$aonline resource$$bcr$$2rdacarrier
001482326 4901_ $$aLecture notes in computer science,$$x1611-3349 ;$$v14307
001482326 500__ $$aInternational conference proceedings.
001482326 500__ $$aIncludes author index.
001482326 5050_ $$aEfficient Annotation and Training Strategies -- Reducing Manual Annotation Costs for Cell Segmentation by Upgrading Low-quality Annotations -- ScribSD: Scribble-supervised Fetal MRI Segmentation based on Simultaneous Feature and Prediction Self-Distillation -- Label-efficient Contrastive Learning-based Model for Nuclei Detection and Classification in 3D Cardiovascular Immunofluorescent Images -- Affordable Graph Neural Network Framework using Topological Graph Contraction -- Approaches for Noisy, Missing, and Low Quality Data -- Dual-domain Iterative Network with Adaptive Data Consistency for Joint Denoising and Few-angle Reconstruction of Low-dose Cardiac SPECT -- A Multitask Framework for Label Refinement and Lesion Segmentation in Clinical Brain Imaging -- COVID-19 Lesion Segmentation Framework for the Contrast-enhanced CT in the Absence of Contrast-enhanced CT Annotation -- Feasibility of Universal Anomaly Detection without Knowing the Abnormality in Medical Image -- Unsupervised, Self-supervised, and Contrastive Learning -- Decoupled Conditional Contrastive Learning with Variable Metadata for Prostate Lesion Detection -- FBA-Net: Foreground and Background Aware Contrastive Learning for Semi-Supervised Atrium Segmentation -- Masked Image Modeling for Label-Efficient Segmentation in Two-Photon Excitation Microscopy -- Automatic Quantification of COVID-19 Pulmonary Edema by Self-supervised Contrastive Learning -- SDLFormer: A Sparse and Dense Locality-enhanced Transformer for Accelerated MR Image Reconstruction -- Robust Unsupervised Image to Template Registration Without Image Similarity Los -- A Dual-Branch Network with Mixed and Self-Supervision for Medical Image Segmentation: An Application to Segment Edematous Adipose Tissue -- Weakly-supervised, Semi-supervised, and Multitask Learning -- Combining Weakly Supervised Segmentation with Multitask Learning for Improved 3D MRI Brain Tumour Classification -- Exigent Examiner and Mean Teacher: An Advanced 3D CNN-based Semi-Supervised Brain Tumor Segmentation Framework -- Extremely Weakly-supervised Blood Vessel Segmentation with Physiologically Based Synthesis and Domain Adaptation -- Multi-Task Learning for Few-Shot Differential Diagnosis of Breast Cancer Histopathology Image -- Active Learning -- Efficient Annotation for Medical Image Analysis: A One-Pass Selective Annotation Approach -- Test-time Augmentation-based Active Learning and Self-training for Label-efficient Segmentation -- Active Transfer Learning for 3D Hippocampus Segmentation -- Transfer Learning -- Using Training Samples as Transitive Information Bridges in Predicted 4D MRI -- To Pretrain or not to Pretrain? A Case Study of Domain-Specific Pretraining for Semantic Segmentation in Histopathology -- Large-scale Pretraining on Pathological Images for Fine-tuning of Small Pathological Benchmarks.
001482326 506__ $$aAccess limited to authorized users.
001482326 520__ $$aThis book consists of full papers presented in the 2nd workshop of ”Medical Image Learning with Noisy and Limited Data (MILLanD)” held in conjunction with the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023). The 24 full papers presented were carefully reviewed and selected from 38 submissions. The conference focused on challenges and limitations of current deep learning methods applied to limited and noisy medical data and present new methods for training models using such imperfect data.
001482326 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed October 12, 2023).
001482326 650_0 $$aImaging systems in medicine$$vCongresses.
001482326 650_0 $$aDeep learning (Machine learning)$$vCongresses.
001482326 650_6 $$aImagerie médicale$$vCongrès.
001482326 650_6 $$aApprentissage profond$$vCongrès.
001482326 655_0 $$aElectronic books.
001482326 655_7 $$aproceedings (reports)$$2aat
001482326 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772
001482326 655_7 $$aConference papers and proceedings.$$2lcgft
001482326 655_7 $$aActes de congrès.$$2rvmgf
001482326 7001_ $$aXue, Zhiyun,$$eeditor.
001482326 7001_ $$aAntani, Sameer K.,$$eeditor.
001482326 7001_ $$aZamzmi, Ghada,$$eeditor.
001482326 7001_ $$aYang, Feng,$$eeditor.
001482326 7001_ $$aRajaraman, Sivaramakrishnan,$$eeditor.
001482326 7001_ $$aHuang, Sharon Xiaolei,$$eeditor.
001482326 7001_ $$aLinguraru, Marius George,$$eeditor.
001482326 7001_ $$aLiang, Zhaohui,$$eeditor.
001482326 7112_ $$aInternational Conference on Medical Image Computing and Computer-Assisted Intervention$$n(26th :$$d2023 :$$cVancouver, B.C.).
001482326 830_0 $$aLecture notes in computer science ;$$v14307.$$x1611-3349
001482326 852__ $$bebk
001482326 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-44917-8$$zOnline Access$$91397441.1
001482326 909CO $$ooai:library.usi.edu:1482326$$pGLOBAL_SET
001482326 980__ $$aBIB
001482326 980__ $$aEBOOK
001482326 982__ $$aEbook
001482326 983__ $$aOnline
001482326 994__ $$a92$$bISE