{"product_id":"machine-learning-in-medical-imaging-9783032095121","title":"Machine Learning in Medical Imaging","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e.- LGE-Guided Cross-Modality Contrastive Learning for Gadolinium-Free Cardiomyopathy Screening in Cine CMR.\u003cbr\u003e.- Facial Model Assisted Shape Prediction for Orthognathic Surgery.\u003cbr\u003e.- Joint Motion Correction of Multi-Atlas Functional Connectivity during Infancy.\u003cbr\u003e.- 3D-ReVert: 3D Reconstruction of Vertebrae from a Single Radiograph for Minimally Invasive Spine Surgery.\u003cbr\u003e.- VIViT: Variable-Input Vision Transformer Framework for 3D MR Image Segmentation.\u003cbr\u003e.- gResDM: A Graph-driven Residual Diffusion Model for Accelerating DWI Data Acquisition.\u003cbr\u003e.- Semi-Supervised 3D Medical Segmentation from 2D Natural Images Pretrained Model.\u003cbr\u003e.- Regional Hausdorff Distance Losses for Medical Image Segmentation.\u003cbr\u003e.- Identification of functional brain dynamics based on structural connectivity constrained functional time series.\u003cbr\u003e.- MR-CLIP: Efficient Metadata-Guided Learning of MRI Contrast Representations.\u003cbr\u003e.- Lightweight Hypercomplex MRI Reconstruction: A Generalized Kronecker-Parameterized Approach.\u003cbr\u003e.- Leveraging self-supervised pretraining using transformers for enhanced lung nodule detection in CT scans.\u003cbr\u003e.- From Action to Anatomy - Countering Data Scarcity with Video-Based Training for Ill-Posed MRI Problems.\u003cbr\u003e.- Auditing Significance, Metric Choice, and Demographic Fairness in Medical AI Challenges.\u003cbr\u003e.- Synthesis of Abdominal Contrast-Enhanced CT using Diffusion-based Spatial Transform Control.\u003cbr\u003e.- AMD-Mamba: A Phenotype-Aware Multi-Modal Framework for Robust AMD Prognosis.\u003cbr\u003e.- DEnPL: Improved Classification in Imbalanced Medical Datasets via Data-Engineered Prototypical Metric Loss.\u003cbr\u003e.- A Detail-preserving Latent Diffusion Model for Arbitrarily Accelerated MR Imaging.\u003cbr\u003e.- TransGATNet: Hybrid Temporal-Frequency Features with Graph-Attention Transformers for Sleep Staging in OSA Patients.\u003cbr\u003e.- U-DFA: A Unified DINOv2-Unet with Dual Fusion Attention for Multi-Dataset Medical Segmentation.\u003cbr\u003e.- UniDis: Universal Distillation for Efficient and Personalized Pathology Diagnosis.\u003cbr\u003e.- End-to-end Cortical Surface Reconstruction from Clinical Magnetic Resonance Images.\u003cbr\u003e.- Brain Network Mamba: A Bi-directional State-Space Model for Brain Network Analysis on rs-fMRI.\u003cbr\u003e.- Emerging Semantic Segmentation from Positive and Negative Coarse Label Learning.\u003cbr\u003e.- ClinicalFMamba: Advancing Clinical Assessment using Mamba-based Multimodal Neuroimaging Fusion.\u003cbr\u003e.- TissueAgeNet: Quantitative Textual Guidance for Tissue Level Brain Age Estimation.\u003cbr\u003e.- Surface-Guided Construction of 4D Volumetric Atlases of Fetal Brains.\u003cbr\u003e.- Radiogenomic Bipartite Graph Representation Learning for Alzheimer's Disease Detection.\u003cbr\u003e.- Feature Imputation for Missing Modalities in Multimodal Ultrasound.\u003cbr\u003e.- CCMorph: Conditional Contrastive Learning for Unsupervised Medical Image Registration.\u003cbr\u003e.- CAC-MAE: A Calcification-aware Masked Autoencoder for Cardiovascular Disease Risk Assessment on Low-Dose CT.\u003cbr\u003e.- HiT-ULM: Hierarchical Temporal Dynamics Learning for Efficient Clinical Ultrasound Localization Microscopy.\u003cbr\u003e.- Policy to Assist Iteratively Local Segmentation: Optimising Modality and Location Selection for Prostate Cancer Localisation.\u003cbr\u003e.- Domain Adaptation for Ulcerative Colitis Severity Estimation Using Patient-Level Diagnoses.\u003cbr\u003e.- AREPAS: Anomaly Detection in Fine-Grained Anatomy with Reconstruction based Semantic Patch Scoring.\u003cbr\u003e.- Beyond Pixels: Medical Image Quality Assessment with Implicit Neural Representations.\u003cbr\u003e.- Medical Referring Image Segmentation: Addressing Multi-Lesion Reference and Annotation Uncertainty via Vision-Language Fusion.\u003cbr\u003e.- Towards Generalizable Clinical Knowledge Discovery for Radiology Report Generation.\u003cbr\u003e.- Weighted Mean Frequencies: a handcraft Fourier feature for 4D Flow MRI segmentation.\u003cbr\u003e.- ConnecToMind: Connectome-Aware fMRI Decoding for Visual Image Reconstruction.\u003cbr\u003e.- Segmentation of glioblastoma infiltration using hybrid labels from MRI and [18F]FET PET.\u003cbr\u003e.- Patch-level attribution of multimodal fracture risk prediction.\u003cbr\u003e.- Scheduled Cross-Domain Multi-Center DINO for Robust High-Content Screening Representation Learning.\u003cbr\u003e.- Temporal Periodic Image Registration with Implicit Neural Representations.\u003cbr\u003e.- Diagnosis of Blood Diseases and Disorders with Topological Deep Learning.\u003cbr\u003e.- Temporal Super-Resolution of Medical Images with Implicit Neural Representations.\u003cbr\u003e.- GyralNet Sub-network Partitioning via Differentiable Spectral Modularity Optimization.\u003cbr\u003e.- GRASPing Anatomy to Improve Pathology Segmentation.\u003cbr\u003e.- Preserving Diagnostic Details in Low-Dose CT with Frequency-Domain Guided Deep Learning.\u003cbr\u003e.- Evaluating structural uncertainty in accelerated MRI: are voxelwise measures useful surrogates?.\u003cbr\u003e.- SwinDDF: Dense Dynamic Fusion Network for 3D Segmentation of Complex-Shaped Nuclei.\u003cbr\u003e.- Improving Visual Search in Medical Videos with Self-Supervised Learning and Temporal Feature Integration.\u003cbr\u003e.- Ontology-Based Concept Distillation for Radiology Report Retrieval and Labeling.\u003cbr\u003e.- Automated Dental Caries Segmentation in Panoramic Radiographs Using Dual-Stage Deep Learning.\u003cbr\u003e.- Anatomy-Guided Semi-Supervised Registration with Combined Rigid and Label Supervision for Improved Rib Deformation Consistency.\u003cbr\u003e.- MultiMAE for Brain MRIs: Robustness to Missing Inputs Using Multi-Modal Masked Autoencoder.\u003cbr\u003e.- Attention Pooling Enhances NCA-based Classification of Microscopy Images.\u003cbr\u003e.- Predicting Cognitive Outcomes by Mapping White Matter Tracts to Surface.\u003cbr\u003e.- Bridging Brain Connectomes and Clinical Reports for Early Alzheimer's Disease Diagnosis.\u003cbr\u003e.- Image-Guided Liver Tumor Synthesis.\u003cbr\u003e.- Relation-Preserving Harmonization of Functional Connectivity Representation: Ensuring Local Functional, Longitudinal, and Population-Level Consistency.\u003cbr\u003e.- nnU-BNST: Deep Learning-Based Automated Segmentation of the Bed Nucleus of the Stria Terminalis.\u003cbr\u003e.- AGFuse-Net: Enhancing Rapid SPECT\/CT Imaging via Anatomy-Guided Attention Gates and Multimodality Fusion Network.\u003cbr\u003e.- GMPT: General Multimodal Segmentation Model Guided by Multi-Expert Pathway.\u003cbr\u003e.- Diff4MMLiTS: Advanced Multimodal Liver Tumor Segmentation via Diffusion-Based Image Synthesis and Alignment.\u003c\/p\u003e","brand":"Springer","offers":[{"title":"Default Title","offer_id":53195539448151,"sku":"9783032095121","price":71.99,"currency_code":"GBP","in_stock":true}],"url":"https:\/\/bookcurl.com\/products\/machine-learning-in-medical-imaging-9783032095121","provider":"Book Curl","version":"1.0","type":"link"}