A review of medical image data augmentation techniques for deep learning applications
Research in artificial intelligence for radiology and radiotherapy has recently become
increasingly reliant on the use of deep learning‐based algorithms. While the performance of …
increasingly reliant on the use of deep learning‐based algorithms. While the performance of …
Machine learning in arrhythmia and electrophysiology
Machine learning (ML), a branch of artificial intelligence, where machines learn from big
data, is at the crest of a technological wave of change sweeping society. Cardiovascular …
data, is at the crest of a technological wave of change sweeping society. Cardiovascular …
Preprocedure application of machine learning and mechanistic simulations predicts likelihood of paroxysmal atrial fibrillation recurrence following pulmonary vein …
Background: Pulmonary vein isolation (PVI) is an effective treatment strategy for patients
with atrial fibrillation (AF), but many experience AF recurrence and require repeat ablation …
with atrial fibrillation (AF), but many experience AF recurrence and require repeat ablation …
Machine learning–enabled multimodal fusion of intra-atrial and body surface signals in prediction of atrial fibrillation ablation outcomes
S Tang, O Razeghi, R Kapoor… - Circulation …, 2022 - Am Heart Assoc
Background: Machine learning is a promising approach to personalize atrial fibrillation
management strategies for patients after catheter ablation. Prior atrial fibrillation ablation …
management strategies for patients after catheter ablation. Prior atrial fibrillation ablation …
Deep learning in the diagnosis and management of arrhythmias
Recent advancements in analyzing methods for the identification of arrhythmia based on
deep learning have revealed great promise towards improving cardiac care. Probabilistic …
deep learning have revealed great promise towards improving cardiac care. Probabilistic …
From images to probabilistic anatomical shapes: A deep variational bottleneck approach
J Adams, S Elhabian - … Conference on Medical Image Computing and …, 2022 - Springer
Statistical shape modeling (SSM) directly from 3D medical images is an underutilized tool for
detecting pathology, diagnosing disease, and conducting population-level morphology …
detecting pathology, diagnosing disease, and conducting population-level morphology …
Fully bayesian vib-deepssm
J Adams, SY Elhabian - … Conference on Medical Image Computing and …, 2023 - Springer
Statistical shape modeling (SSM) enables population-based quantitative analysis of
anatomical shapes, informing clinical diagnosis. Deep learning approaches predict …
anatomical shapes, informing clinical diagnosis. Deep learning approaches predict …
DeepSSM: A blueprint for image-to-shape deep learning models
Statistical shape modeling (SSM) characterizes anatomical variations in a population of
shapes generated from medical images. Statistical analysis of shapes requires consistent …
shapes generated from medical images. Statistical analysis of shapes requires consistent …
Uncertain-deepssm: From images to probabilistic shape models
Statistical shape modeling (SSM) has recently taken advantage of advances in deep
learning to alleviate the need for a time-consuming and expert-driven workflow of anatomy …
learning to alleviate the need for a time-consuming and expert-driven workflow of anatomy …
Deep learning-based multimodal fusion of the surface ECG and clinical features in prediction of atrial fibrillation recurrence following catheter ablation
Y Qiu, H Guo, S Wang, S Yang, X Peng… - BMC Medical Informatics …, 2024 - Springer
Background Despite improvement in treatment strategies for atrial fibrillation (AF), a
significant proportion of patients still experience recurrence after ablation. This study aims to …
significant proportion of patients still experience recurrence after ablation. This study aims to …