Michael Mayhew (Inflammatix)
Neural networks, deep and otherwise, have recently set the standard for performance in a number of challenging tasks in the disciplines of computer vision, natural language processing, speech recognition, and so on. These highly expressive, graphical models have also proven effective in a range of tasks in biology and medicine. Despite the many application successes of neural networks, theory and even empirical best practices for specification and selection of these models is still under development. Current practices in the field, while effective, could be (and in some cases have already been) enhanced with a statistical approach. We believe there is an opportunity for researchers from statistical and mathematical disciplines to contribute and adapt their perspectives and techniques for model specification, estimation of predictive uncertainty, incorporation of prior/domain information and other common statistical tasks to the field of neural networks. This working group aimed at creating an updated (Cheng and Titterington did it back in 1994), open, and community-driven review article that bridged exciting recent developments in statistical and neural network modeling as well as at their interface. The review highlighted state-of-the-art statistics-based neural network models, developed rough guidelines for carrying out routine statistical tasks in the neural network context, and listed software and data resources for the development and application of neural network models in biomedical research. The working group leveraged this knowledge to develop and evaluate neural network models for detection of structural abnormalities in a publicly available set of musculo-skeletal radiographs.
Questions: email email@example.com