… Our deep learning model is significantly faster than classification based on regions of
interest. On average, for each MRI, our deep learning model requires 0.07 s (plus 7 min for NMI …

… Our results showed that our model generalized well to datasets acquired with similar protocols
as the training data, but substantially worse in clinical cohorts with visibly different tissue …

… ) offers new possibilities for individualized radiotherapy planning as it provides spatially
and temporally aligned structural and functional information in a single examination. The …

… which develop sophisticated deep learning approaches to … We also highlighted the importance
of the network structure … create a cardiovascular magnetic resonance imaging reference …

… functional MRI) datasets, we identify highly replicable, generalizable, … whether and how these
structural differences translate to … The increasing availability of resting-state functional MRI (…

… structure and function using machine learning (ML) has great potential, but is currently hindered
by poor generalizability. … While cardiovascular magnetic resonance imaging to measure …

… In this paper, for the first time, we propose an evaluation method for deep learning models
… images and Rician noise for T1w MRI). For fairness, the Structural Similarity (SSIM) between …

… structure (… generalizability of CNN-based stratification models. Although this might be
intuitive, this study quantitatively assessed the effect of the type of input that a CNN-based model …

… the generalizability of deep learning methods, by applying various augmentation strategies,
few-shot learning (… As in prior work presented in IPMI 2021, a multimodal structural 3T MRI …

… shows structure of our model, … the generalizability of the model. Further studies on multiple
vendor platforms and at different field strengths are necessary to fully assess generalizability …