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.
Inhaltsverzeichnis
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.
