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Found 8 papers, 3 papers with code
Heterogeneous Image-based Classification Using Distributional Data Analysis
Our analysis reveals that when coupled with global structural radiomics features derived from the corresponding T1-MRI scans, the proposed smoothed quantile distributions derived from EPM images showed considerable improvements in sensitivity and comparable specificity in contrast to classification based on routinely used summary measures that do not account for image heterogeneity.
Feature Extraction for Generative Medical Imaging Evaluation: New Evidence Against an Evolving Trend
A recent trend is to adapt FID to medical imaging through feature extractors trained on medical images.
Ranked #1 on
Medical Image Generation
on SLIVER07
Bayesian longitudinal tensor response regression for modeling neuroplasticity
Subsequently, we apply the approach to a longitudinal Aphasia dataset consisting of task functional MRI images from a group of subjects who were administered either a control intervention or intention treatment at baseline and were followed up over subsequent visits.
Accounting for Temporal Variability in Functional Magnetic Resonance Imaging Improves Prediction of Intelligence
We compare the prediction performance for different intelligence measures based on static FC, dynamic FC, and region level time series acquired from the Adolescent Brain Cognitive Development (ABCD) study involving close to 7000 individuals.
High-dimensional Measurement Error Models for Lipschitz Loss
Our estimator is designed to minimize the $L_1$ norm among all estimators belonging to suitable feasible sets, without requiring any knowledge of the noise distribution.
Evaluating the Performance of StyleGAN2-ADA on Medical Images
Our computational ablation study revealed that transfer learning and data augmentation stabilize training and improve the perceptual quality of the generated images.
Ranked #1 on
Medical Image Generation
on ACDC
Elastic Shape Analysis of Brain Structures for Predictive Modeling of PTSD
Predictive modeling with such interactions is of paramount interest in heterogeneous mental disorders such as PTSD, where trauma exposure interacts with brain shape changes to influence behavior.
Integrative Learning for Population of Dynamic Networks with Covariates
Although there is a rapidly growing literature on dynamic connectivity methods, the primary focus has been on separate network estimation for each individual, which fails to leverage common patterns of information.
Methodology
