Medical Image Understanding and Analysis

28th Annual Conference, MIUA 2024, Manchester, UK, July 24–26, 2024, Proceedings, Part II

AvMoi Hoon Yap,Connah Kendrick

Häftad, Engelska, 2024

Del 14860 i serien Lecture Notes in Computer Science

814 kr

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2024-07-24
Mått:155 x 235 x 26 mm
Vikt:721 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:458
Förlag:Springer International Publishing AG
ISBN:9783031669576

Utforska kategorier

Systemvetenskap och AI inom Data och IT
Informationsteknik: allmänt inom Data och IT
Tillämpad datateknik inom Data och IT

Innehållsförteckning

.- 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.