-- 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.

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.