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Produktbild: Simplifying Medical Ultrasound
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Simplifying Medical Ultrasound 5th International Workshop, ASMUS 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024, Proceedings

Aus der Reihe Lecture Notes in Computer Science
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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

05.10.2024

Herausgeber

Alberto Gomez + weitere

Verlag

Springer

Seitenzahl

233

Maße (L/B/H)

23,5/15,5/1,4 cm

Gewicht

388 g

Sprache

Englisch

ISBN

978-3-031-73646-9

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

05.10.2024

Herausgeber

Verlag

Springer

Seitenzahl

233

Maße (L/B/H)

23,5/15,5/1,4 cm

Gewicht

388 g

Sprache

Englisch

ISBN

978-3-031-73646-9

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: GPSR Kontakt

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  • Produktbild: Simplifying Medical Ultrasound

  • .- Image Acquisition, Synthesis and Enhancement.

    .- Unsupervised Physics-Inspired Shear Wave Speed Estimation in Ultrasound Elastography.

    .- Simplifying Prostate Elastography Using Micro-Ultrasound and Transfer Function Imaging.

    .- Do High-Performance Image-to-Image Translation Networks Enable the Discovery of Radiomic Features? Application to MRI Synthesis from Ultrasound in Prostate Cancer.

    .- PHOCUS: Physics-Based Deconvolution for Ultrasound Resolution Enhancement.

    .- Tracking, Registration and Image-guided Interventions.

    .- PIPsUS: Self-Supervised Point Tracking in Ultrasound.

    .- Structure-aware World Model for Probe Guidance via Large-scale Selfsupervised Pre-train.

    .- An Evaluation of Low-Cost Hardware on 3D Ultrasound Reconstruction Accuracy.

    .- Learning to Match 2D Keypoints Across Preoperative MR and Intraoperative Ultrasound.

    .- Automatic facial axes standardization of 3D fetal ultrasound images.

    .- Segmentation.

    .- C-TRUS: A Novel Dataset and Initial Benchmark For Colon Wall Segmentation in Transabdominal Ultrasound.

    .- Label Dropout: Improved Deep Learning Echocardiography Segmentation Using Multiple Datasets With Domain Shift and Partial Labelling.

    .- Introducing Anatomical Constraints in Mitral Annulus Segmentation in Transesophageal Echocardiography.

    .- Interactive Segmentation Model for Placenta Segmentation from 3D Ultrasound Images.

    .- Enhanced Uncertainty Estimation in Ultrasound Image Segmentation with MSU-Net.

    .- Classification and Detection.

    .- Multi-Site Class-Incremental Learning with Weighted Experts in Echocardiography.

    .- Masked autoencoders for medical ultrasound videos using ROI-aware masking.

    .- Uncertainty-based Multi-modal Learning for Myocardial Infarction Diagnosis using Echocardiography and Electrocardiograms.

    .- Fetal Ultrasound Video Representation Learning using Contrastive Rubik’s Cube Recovery.

    .- LoRIS - Weakly-supervised Anomaly Detection for Ultrasound Images.

    .- Unsupervised Detection of Fetal Brain Anomalies using Denoising Diffusion Models.

    .- Diffusion Models for Unsupervised Anomaly Detection in Fetal Brain Ultrasound.