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To better support engineering students and to create an inclusive and welcoming educational context, it is necessary to reimagine instructional methods and approaches. In contrast to deficit educational models that focus on perceptions of what students lack, asset-based practices focus on how students’ lived experiences can be used to enrich and strengthen their educational experiences. There is a need to support faculty in adopting existing techniques or developing new techniques in undergraduate courses, as most existing literature related to asset-based practices is focused on K-12 settings. Engineering design courses provide an ideal context for asset-based practices because the design process requires a diverse set of knowledge, experiences, and skills. Guided by self-determination theory, an understanding of implicit bias and stereotype threat, and the large existing body of research on asset-based pedagogy, we seek to support engineering student outcomes by empowering faculty with tools and strategies to incorporate asset-based practices in their courses. We are engaged in a three-year project focused on assessing the impact of asset-based practices in engineering design courses a large, public, land-grant, Hispanic-serving institution in the southwestern United States, funded by the NSF IUSE:EDU program. Here, we will summarize the design and results from our professional development for faculty, including theoretical frameworks and evidence guiding our work. We share content from our professional development, summarizing learning objectives, presentation content, and activities. Additionally, we present comments shared by instructors related to our professional development, including common barriers to implementing educational innovations in their courses. Our work will provide insights to practitioners interested in promoting inclusive classroom practices in engineering education and researchers who are translating research to practice, especially through professional development. 

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 learning contributed to better performance in diagnosis, classification, segmentation of different neuroimaging diseases and problems, than methods without transfer learning. Among different transfer learning approaches, fine-tuning all convolutional and fully-connected layers approach and freezing convolutional layers and fine-tuning fully-connected layers approach demonstrated superior performance in terms of accuracy. These recent transfer learning approaches not only show great performance but also require less computational resources and time.