Medical image understanding and anal...
Medical Image Understanding and Analysis (Conference) (2024 :)

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  • Medical image understanding and analysis = 28th Annual Conference, MIUA 2024, Manchester, UK, July 24-26, 2024 : proceedings.. Part II /
  • 紀錄類型: 書目-電子資源 : Monograph/item
    正題名/作者: Medical image understanding and analysis/ edited by Moi Hoon Yap ... [et al.].
    其他題名: 28th Annual Conference, MIUA 2024, Manchester, UK, July 24-26, 2024 : proceedings.
    其他題名: MIUA 2024
    其他作者: Yap, Moi Hoon.
    團體作者: Medical Image Understanding and Analysis (Conference)
    出版者: Cham :Springer Nature Switzerland : : 2024.,
    面頁冊數: xx, 458 p. :ill. (chiefly col.), digital ;24 cm.
    內容註: 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.
    Contained By: Springer Nature eBook
    標題: Diagnostic imaging - Congresses. -
    電子資源: https://doi.org/10.1007/978-3-031-66958-3
    ISBN: 9783031669583
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