Medical Image Understanding and Analysis
Description
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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Content
.- 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.
