Medical Image Understanding and Analysis
Springer International Publishing (Verlag)
978-3-031-66957-6 (ISBN)
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.
.- 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.
| Erscheinungsdatum | 25.07.2024 |
|---|---|
| Reihe/Serie | Lecture Notes in Computer Science |
| Zusatzinfo | XX, 458 p. 172 illus., 153 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Schlagworte | AI generalisation • AI in medical imaging • brain imaging • Cardiac Imaging • Computational Models • computer vision • Deep learning • Dermatology • Digital Pathology • Domain adaptation for medical imaging • Image Processing • machine learning • Medical Image Analysis • microscopic imaging |
| ISBN-10 | 3-031-66957-6 / 3031669576 |
| ISBN-13 | 978-3-031-66957-6 / 9783031669576 |
| Zustand | Neuware |
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