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Herausgeber: 
  • Christopher MacGowan
  • Christian Baumgartner
  • William M. Wells
  • Adrian Dalca
  • Andrew Melbourne
  • Roxane Licandro
  • Jana Hutter
  • Esra Abaci Turk
  • Carole H. Sudre
  • Ryutaro Tanno
  • Jordina Torrents Barrena
  • Koen van Leemput
  • Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis: 3rd Internationa 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 2 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   i.d.R. innert 5-10 Tagen versandfertig
    Veröffentlichung:  Oktober 2021  
    Genre:  EDV / Informatik 
     
    ArtificialIntelligence / bioinformatics / Computervision / DeepLearning / imageanalysis / imageprocessing / imagequality / imagereconstruction
    ISBN:  9783030877347 
    EAN-Code: 
    9783030877347 
    Verlag:  Springer 
    Einband:  Kartoniert  
    Sprache:  English  
    Dimensionen:  H 235 mm / B 155 mm / D 17 mm 
    Gewicht:  476 gr 
    Seiten:  312 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    UNSURE 2021 - Uncertainty estimation and modelling and annotation uncertainty.- Model uncertainty estimation for medical Imaging based diagnosis.- Accurate simulation of operating system updates in neuroimaging using Monte-Carlo arithmetic.- Leveraging uncertainty estimates to improve segmentation performance in cardiac MR.- Improving the reliability of semantic segmentation of medical images by uncertainty modelling with Bayesian deep networks and curriculum learning.- Unpaired MR image homogeneisation by disentangled representations and its uncertainty.- Uncertainty-aware deep learning based deformable registration.- Monte Carlo Concrete DropPath for Epistemic Uncertainty Estimation in Brain Tumour segmentation.- Improving Aleatoric Uncertainty quantification in multi-annotated medical image segmentation with normalizing flows.- UNSURE 2021 - Domain shift robustness and risk management in clinical pipelines.- Task-agnostic out-of-distribution detection using kernel density estimation.- Out of distribution detection for medical images.- Robust selective classification of skin lesions with asymmetric costs.- Confidence-based Out-of-Distribution detection: a comparative study and analysis.- Novel disease detection using ensembles with regularized disagreement.- PIPPI2021.- Automatic Placenta Abnormality Detection using Convolutional Neural Networks on Ultrasound Texture.- Simulated Half-Fourier Acquisitions Single-shot Turbo Spin Echo (HASTE) of the Fetal Brain: Application to Super-Resolution Reconstruction.- Spatio-temporal atlas of normal fetal craniofacial feature development and CNN-based ocular biometry for motion-corrected fetal MRI.- Myelination of preterm brain networks at adolescence.- A bootstrap self-training method for sequence transfer: State-of-the-art placenta segmentation in fetal MRI.- Segmentation of the cortical plate in fetal brain MRI with a topological loss.- Fetal brain MRI measurements using a deep learning landmark network with reliability estimation.- CAS-Net: Conditional Atlas Generation and Brain Segmentation for Fetal MRI.- Detection of Injury and Automated Triage of Preterm Neonatal MRI using Patch-Based Gaussian Processes.- Assessment of Regional Cortical Development through Fissure Based Gestational Age Estimation in 3D Fetal Ultrasound.- Texture-based Analysis of Fetal Organs in Fetal Growth Restriction.- Distributionally Robust Segmentation of Abnormal Fetal Brain 3D MRI.- Analysis of the Anatomical Variability of Fetal Brains with Corpus Callosum Agenesis.- Predicting preterm birth using multimodal fetal imaging.

      



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