Medical Image Understanding and Analysis〈1st ed. 2024〉 : 27th Annual Conference, MIUA 2023, Aberdeen, UK, July 19–21, 2023, Proceedings

著者名：Waiter, Gordon (EDT)/Lambrou, Tryphon (EDT)/Leontidis, Georgios (EDT)/Oren, Nir (EDT)/Morris, Teresa (EDT)/Gordon, Sharon (EDT)

Springer（2023/12/01発売）

• 言語：ENG
• ISBN：9783031485923
• eISBN：9783031485930

Description

This book constitutes the proceedings of the 27th Annual Conference on Medical Image Understanding and Analysis, MIUA 2023, which took place in Aberdeen, UK, during July 19–21, 2023.

The 24 full papers presented in this book were carefully reviewed and selected from 42 submissions. They were organized in topical sections as follows: Image interpretation; radiomics, predictive models and quantitative imaging; image classification; and biomarker detection.

Table of Contents

Segmentation of White Matter Hyperintensities and Ischaemic Stroke Lesions in Structural MRI.- A Deep Learning Based Approach to Semantic Segmentation of Lung Tumour Areas in Gross Pathology Images.- Iterative Refinement Algorithm for Liver Segmentation Ground-Truth Generation using Fine-Tuning Weak Labels for CT and Structural MRI.- M-VAAL: Multimodal Variational Adversarial Active Learning for Downstream Medical Image Analysis Tasks.- BliMSR: Blind degradation modelling for generating high-resolution medical images.- Efficient Semantic Segmentation of Nuclei in Histopathology Images Using Segformer.- Cross-Modality Deep Transfer Learning: Application to Liver Segmentation in CT and MRI.- Can SegFormer be a True Competitor to U-Net for Medical Image Segmentation.- Harnessing the Potential of Deep Learning for Total Shoulder Implant Classification: A Comparative Study.- Deep Facial Phenotyping with Mixup Augmentation.- Context Matters:Cross-domain Cell Detection in Histopathology Images via Contextual Regularization.- TON-ViT: A Neuro-Symbolic AI based on Task Oriented Network with a Vision Transformer.- A new similarity metric for deformable registration of MALDI-MS and MRI images.- Decoding Individual and Shared Experiences of Media Perception using CNN architectures.- Revolutionizing Cancer Diagnosis through Hybrid Self-supervised Deep Learning: EfficientNet with Denoising Autoencoder for Semantic Segmentation of Histopathological Images.- Baseline Models for Action Recognition of Unscripted Casualty Care Dataset.- 

Web-based AI System for Medical Image Segmentation.- A new approach for identifying skin diseases from dermatological RGB images using source separation.- Pseudo-SPR map Generation from MRI using U-Net Architecture for Ion Beam Therapy Application.- Generalised 3D Medical Image Registration with Learned Shape Encodings.- Retinal Image Screening with Topological Machine Learning.- Neural Network Pruning for Real-time Polyp Segmentation.- A Novel Approach to Breast Cancer Segmentation using U-Net Model with Attention Mechanisms and FedProx Algorithm.- Super Images - A New 2D Perspective on 3D Medical Imaging Analysis.