Cham : Springer Nature Switzerland : Imprint: Springer, 2024
1st ed. 2024.
Table of Contents:
“…GEMTrans:
A General, Echocardiography-based, Multi-Level Transformer Framework for Cardiovascular Diagnosis -- Unsupervised Anomaly Detection in Medical Images with
a Memory-augmented Multi-level Cross-attentional Masked Autoencoder -- LMT: Longitudinal Mixing Training
a Framework for the Prediction of Disease Progression Using
a Single Image -- Identifying Alzheimer's Disease-induced Topology Alterations in Structural Networks using Convolutional Neural Networks -- Specificity-Aware Federated Graph Learning for Brain Disorder Analysis with Functional MRI -- 3D Transformer Based on Deformable Patch Location for Differential Diagnosis Between Alzheimer’s Disease and Frontotemporal Dementia -- Consisaug:
A Consistency-based Augmentation for Polyp Detection in Endoscopy Image Analysis -- Cross-view Contrastive Mutual Learning across Masked Autoencoders for Mammography Diagnosis -- Modeling Life-span Brain Age from Large-scale Dataset based on Multi-level Information Fusion -- Boundary-Constrained Graph Network for Tooth Segmentation on 3D Dental Surfaces -- FAST-Net:
A Coarse-to-fine Pyramid Network for Face-Skull Transformation -- Mixing Histopathology Prototypes into Robust Slide-Level Representations for Cancer Subtyping -- Consistency Loss for Improved Colonoscopy Landmark Detection with Vision Transformers -- Radiomics Boosts Deep Learning Model for IPMN Classification -- Class-Balanced Deep Learning with Adaptive Vector Scaling Loss for Dementia Stage Detection -- Enhancing Anomaly Detection in Melanoma Diagnosis through Self-Supervised Training and Lesion Comparison -- DynBrainGNN: Towards Spatio-Temporal Interpretable Graph Neural Network based on Dynamic Brain Connectome for Psychiatric Diagnosis -- Precise localization within the GI tract by combining classification of CNNs and time-
series analysis of HMMs -- Towards Unified Modality Understanding for Alzheimer's Disease Diagnosis using Incomplete Multi-Modality Data -- COVID-19 Diagnosis Based on Swin Transformer Model with Demographic Information Fusion and Enhanced Multi-head Attention Mechanism -- MoViT: Memorizing Vision Transformers for Medical Image Analysis -- Fact-Checking of AI-Generated Reports -- Is Visual Explanation with Grad-CAM More Reliability for Deeper Neural Networks? …”
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