Medical Image Understanding and Analysis [electronic resource] : 28th Annual Conference, MIUA 2024, Manchester, UK, July 24-26, 2024, Proceedings, Part I / edited by Moi Hoon Yap, Connah Kendrick, Ardhendu Behera, Timothy Cootes, Reyer Zwiggelaar.

-- 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.

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