Medical Image Understanding and Analysis

28th Annual Conference, MIUA 2024, Manchester, UK, July 24¿26, 2024, Proceedings, Part I
Herausgegeben:Yap, Moi Hoon; Kendrick, Connah; Behera, Ardhendu; Cootes, Timothy; Zwiggelaar, Reyer

• Broschiertes Buch

Produktbeschreibung

This two-volume set LNCS 14859-14860 constitutes the proceedings of the 28th Annual Conference on Medical Image Understanding and Analysis, MIUA 2024, held in Manchester, UK, during July 24-26, 2024.

The 59 full papers included in this book were carefully reviewed and selected from 93 submissions. They were organized in topical sections as follows:

Part I : Advancement in Brain Imaging; Medical Images and Computational Models; and Digital Pathology, Histology and Microscopic Imaging.

Part II : Dental and Bone Imaging; Enhancing Low-Quality Medical Images; Domain Adaptation and Generalisation; and Dermatology, Cardiac Imaging and Other Medical Imaging.

Produktdetails

• Lecture Notes in Computer Science 14859
• Verlag: Springer / Springer Nature Switzerland / Springer, Berlin
• Artikelnr. des Verlages: 978-3-031-66954-5
• 2024
• Seitenzahl: 444
• Erscheinungstermin: 24. Juli 2024
• Englisch
• Abmessung: 235mm x 155mm x 24mm
• Gewicht: 663g
• ISBN-13: 9783031669545
• ISBN-10: 3031669541
• Artikelnr.: 70986904

Inhaltsangabe

.- Advancement in Brain Imaging.

.- Robust Multi-Modal Registration of Cerebral Vasculature.

.- Towards Segmenting Cerebral Arteries from Structural MRI.

.- Stochastic Uncertainty Quantification techniques fail to account for Inter-Analyst Variability in White Matter Hyperintensity segmentation.

.- Learning-based MRI Response Predictions from OCT Microvascular Models to Replace Simulation-based Frameworks.

.- Multimodal 3D Brain Tumor Segmentation with Adversarial Training and Conditional Random Field.

.- DeepDSMRI: Deep Domain Shift analyzer for MRI.

.- Self-Supervised Pretraining for Cortial Surface Analysis.

.- Spike Detection in Deep Brain Stimulation Surgery with Convolutional Neural Networks.

.- Medical Images and Computational Models.

.- Micro-CT Imaging Techniques for Visualizing Pinniped Mystacial Pad Musculature.

.- SCorP: Statistics-Informed Dense Correspondence Prediction Directly from Unsegmented Medical Images.

.- JointViT: Modeling Oxygen Saturation Levels with Joint Supervision on Long-Tailed OCTA.

.- Identification of skin diseases based on blind chromophore separation and artificial intelligence.

.- Generating Chest Radiology Report Findings using a Multimodal Method.

.- Image processing and machine learning techniques for Chagas disease detection and identification.

.- Ensemble deep learning models for segmentation of prostate zonal anatomy and pathologically suspicious area.

.- U-Net-driven image reconstruction for range verification in proton therapy.

.- DynaMMo: Dynamic Model Merging for Efficient Class Incremental Learning for Medical Images.

.- PDSE: A Multiple Lesion Detector for CT Images Using PANet and Deformable Squeeze-and-Excitation Block.

.- What is the Best Way to Fine-tune Self-supervised Medical Imaging Models.

.- Digital Pathology, Histology and Microscopic Imaging.

.- RoTIR: Rotation-Equivariant Network and Transformers for Zebrafish Scale Image Registration.

.- GRU-Net: Gaussian attention aided dense skip connection based multiResU-Net for Breast Histopathology Image Segmentation.

.- Bounding Box is all you need: Learning to Segment Cells in 2D Microscopic Images via Box Annotations.

.- Leveraging Foundation Models for Enhanced Detection of Colorectal Cancer Biomarkers in Small Datasets.

.- SPADESegResNet: Harnessing Spatially-adaptive Normalization for Breast Cancer Semantic Segmentation.

.- Unsupervised Anomaly Detection on Histopathology Images Using Adversarial Learning and Simulated Anomaly.

.- Nuclei-Location Based Point Set Registration of Multi-Stained Whole Slide Images.

.- CellGenie: An end-to-end Pipeline for Synthetic Cellular Data Generation and Segmentation: A Use Case for Cell Segmentation in Microscopic Images.

.- A Line Is All You Need: Weak Supervision For 2.5D Cell Segmentation.