Identity change and the human dissection experience over the first year of medical training

The aim of this study is to explore identity change in medical students over their first year of medical training, particularly in relation to their experience of human dissection. Each of our four participants completed two repertory grids at the end of term one and, again, towards the end of term three. One grid tapped their identity construction, and the other, their experience of human dissection. Our participants were optimistic about becoming similar to a doctor they admired and, towards the end of term three, began to develop a stable identity as a medical student. Their identity constructs involved three common themes: dedication, competence, and responsibility. However, the data also revealed negative reactions to the demands of training, such as feeling driven and stressed. Three major themes were apparent in their experience of human dissection: involvement, emotional coping, and ability. Our participants’ dedication to their studies was reflected in their appreciation of the need to become involved actively in the process of dissection but some experienced an erosion of their self-confidence and perceived some of their colleagues to have lost much of their enthusiasm for learning. Emotional coping could be an additional challenge within this context and their reaction tended to reflect distancing processes previously identified in the literature. In all, we see a development of a vulnerable sense of professionalism alongside a frustration of losing out potentially on wider aspects of personal development due to the high work demands

Anatomy-specific classification of medical images using deep convolutional nets

Automated classification of human anatomy is an important prerequisite for many computer-aided diagnosis systems. The spatial complexity and variability of anatomy throughout the human body makes classification difficult. "Deep learning" methods such as convolutional networks (ConvNets) outperform other state-of-the-art methods in image classification tasks. In this work, we present a method for organ- or body-part-specific anatomical classification of medical images acquired using computed tomography (CT) with ConvNets. We train a ConvNet, using 4,298 separate axial 2D key-images to learn 5 anatomical classes. Key-images were mined from a hospital PACS archive, using a set of 1,675 patients. We show that a data augmentation approach can help to enrich the data set and improve classification performance. Using ConvNets and data augmentation, we achieve anatomy-specific classification error of 5.9 % and area-under-the-curve (AUC) values of an average of 0.998 in testing. We demonstrate that deep learning can be used to train very reliable and accurate classifiers that could initialize further computer-aided diagnosis.Comment: Presented at: 2015 IEEE International Symposium on Biomedical Imaging, April 16-19, 2015, New York Marriott at Brooklyn Bridge, NY, US

GridNet with automatic shape prior registration for automatic MRI cardiac segmentation

In this paper, we propose a fully automatic MRI cardiac segmentation method based on a novel deep convolutional neural network (CNN) designed for the 2017 ACDC MICCAI challenge. The novelty of our network comes with its embedded shape prior and its loss function tailored to the cardiac anatomy. Our model includes a cardiac centerof-mass regression module which allows for an automatic shape prior registration. Also, since our method processes raw MR images without any manual preprocessing and/or image cropping, our CNN learns both high-level features (useful to distinguish the heart from other organs with a similar shape) and low-level features (useful to get accurate segmentation results). Those features are learned with a multi-resolution conv-deconv "grid" architecture which can be seen as an extension of the U-Net. Experimental results reveal that our method can segment the left and right ventricles as well as the myocardium from a 3D MRI cardiac volume in 0.4 second with an average Dice coefficient of 0.90 and an average Hausdorff distance of 10.4 mm.Comment: 8 pages, 1 tables, 2 figure

1st INCF Workshop on Sustainability of Neuroscience Databases

The goal of the workshop was to discuss issues related to the sustainability of neuroscience databases, identify problems and propose solutions, and formulate recommendations to the INCF. The report summarizes the discussions of invited participants from the neuroinformatics community as well as from other disciplines where sustainability issues have already been approached. The recommendations for the INCF involve rating, ranking, and supporting database sustainability