Deep learning algorithms have been moderately successful in diagnoses of diseases by analyzing medical images especially through neuroimaging that is rich in annotated data. Transfer learning methods have demonstrated strong performance in tackling annotated data. It utilizes and transfers knowledge learned from a source domain to target domain even when the dataset is small. There are multiple approaches to transfer learning that result in a range of performance estimates in diagnosis, detection, and classification of clinical problems. Therefore, in this paper, we reviewed transfer learning approaches, their design attributes, and their applications to neuroimaging problems. We reviewed two main literature databases and included the most relevant studies using predefined inclusion criteria. Among 50 reviewed studies, more than half of them are on transfer learning for Alzheimer's disease. Brain mapping and brain tumor detection were second and third most discussed research problems, respectively. The most common source dataset for transfer learning was ImageNet, which is not a neuroimaging dataset. This suggests that the majority of studies preferred pre-trained models instead of training their own model on a neuroimaging dataset. Although, about one third of studies designed their own architecture, most studies used existing Convolutional Neural Network architectures. Magnetic Resonance Imaging was the most common imaging modality. In almost all studies, transfer learni...

Neuroimaging datasets keep growing in size to address increasingly complex medical questions. However, even the largest datasets today alone are too small for training complex models or for finding genome wide associations. A solution is to grow the sample size by merging data across several datasets. However, bias in datasets complicates this approach and includes additional sources of variation in the data instead. In this work, we combine 15 large neuroimaging datasets to study bias. First, we detect bias by demonstrating that scans can be correctly assigned to a dataset with 73.3% accuracy. Next, we introduce metrics to quantify the compatibility across datasets and to create embeddings of neuroimaging sites. Finally, we incorporate the presence of bias for the selection of a training set for predicting autism. For the quantification of the dataset bias, we introduce two metrics: the Bhattacharyya distance between datasets and the age prediction error. The presented embedding of neuroimaging sites provides an interesting new visualization about the similarity of different sites. This could be used to guide the merging of data sources, while limiting the introduction of unwanted variation. Finally, we demonstrate a clear performance increase when incorporating dataset bias for training set selection in autism prediction. Overall, we believe that the growing amount of neuroimaging data necessitates to incorporate data-driven methods for quantifying dataset bias in future analyses.