Full 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.
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.
