About the Book

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.

Table of Contents:
.- Dental and Bone Imaging. .- Enhancing Cephalometric Landmark Detection with a Two-Stage Cascaded CNN on Multi-Resolution Multi-Modal Data. .- Enhancing Dental Diagnostics: Advanced Image Segmentation Models for Teeth Identification and Enumeration. .- 3D Bone Shape from CT-Scans Provides an Objective Measure of Osteoarthritis Severity: data from the IMI-APPROACH study. .- CNN-based osteoporotic vertebral fracture prediction and risk assessment on MrOS CT data: Impact of CNN model architecture. .- Analysis of leg bones from whole body DXA in the UK Biobank. .- H-FCBFormer: Hierarchical Fully Convolutional Branch Transformer for Occlusal Contact Segmentation with Articulating Paper. .- Enhancing Low-Quality Medical Images. .- Ultrasound Confidence Maps with Neural Implicit Representation. .- Blurry Boundary Segmentation with Semantic-guided Feature Learning. .- SA-GCN: Scale Adaptive Graph Convolutional Network for ASD Identification. .- Resolution-Invariant Medical Image Segmentation using Fourier Neural Operators. .- YOLO-TL:A Tiny Object Segmentation Framework for Low Quality Medical Images. .- Superresolution of real-world multiscale bone CT verified with clinical bone measures. .- Reconstructing MRI parameters using a noncentral chi noise model. .- Domain Adaptation and Generalisation. .- AdaptiveSAM: Towards Efficient Tuning of SAM for Surgical Scene Segmentation. .- Analysing Variables for 90-Day Functional-Outcome Prediction of Endovascular Thrombectomy. .- Multimodal Deformable Image Registration for Long-COVID Analysis Based on Progressive Alignment and Multi-perspective Loss. .- Confounder-Aware Image Synthesis for Pathology Segmentation in New Magnetic Resonance Imaging Sequences. .- Prediction of total metabolic tumor volume from tissue-wise FDG-PET/CT projections, interpreted using cohort saliency analysis. .- Expert model prediction through feature matching. .- Enhancing Cross-Institute Generalisation of GNNs in Histopathology through Multiple Embedding Graph Augmentation (MEGA). .- PMT: Partial-Modality Translation Based on Diffusion Models for Prostate Magnetic Resonance and Ultrasound Image Registration. .- Fine-grained Medical Image Synthesis with Dual-Attention Adversarial Learning. .- Dermatology, Cardiac Imaging and Other Medical Imaging. .- Enhancing Skin Lesion Classification: A Self-Attention Fusion Approach with Vision Transformer. .- Optimizing Melanoma Prognosis through Synergistic Preprocessing and Deep Learning Architecture for Dermoscopic Thickness Prediction. .- The Effect of Image Preprocessing Algorithms on Diabetic Foot Ulcer Classification. .- Synthetic Balancing of Cardiac MRI Datasets. .- EchoVisuAL: Efficient Segmentation of Echocardiograms using Deep Active Learning. .- Improving Automated Ultrasound Infant Hip Screening using an Integrated Clinical Classification Loss. .- Deep learning models to automate the scoring of hand radiographs for Rheumatoid Arthritis. .- Radiomic Analysis for Prediction of Preterm Birth. .- Hierarchical multi-label learning for musculoskeletal phenotyping in mice. .- MIUA 2023 Overlooked Paper. .- Prediction of Incident Atrial Fibrillation in Population with Ischemic Heart Disease using Machine Learning with Radiomics and ECG Markers.