We introduce a fast and efficient convolutional neural network, ESPNet, for semantic segmentation of high resolution images under resource constraints. ESPNet is based on a new convolutional module, efficient spatial pyramid (ESP), which is efficient in terms of computation, memory, and power. ESPNet is 22 times faster (on a standard GPU) and 180 times smaller than the state-of-the-art semantic segmentation network PSPNet, while its category-wise accuracy is only 8% less. We evaluated EPSNet on a variety of semantic segmentation datasets including Cityscapes, PASCAL VOC, and a breast biopsy whole slide image dataset. Under the same constraints on memory and computation, ESPNet outperforms all the current efficient CNN networks such as MobileNet, ShuffleNet, and ENet on both standard metrics and our newly introduced performance metrics that measure efficiency on edge devices. Our network can process high resolution images at a rate of 112 and 9 frames per second on a standard GPU and edge device, respectively.

In order to create a successful animated story, the emotional state of a character must be staged so that it is unmistakable and clear. The viewer's perception of its facial expressions is key to successfully staging the emotion. Traditionally animators and automatic expression transfer systems rely on geometric markers and features modeled on human faces to create character expressions, yet these features do not accurately transfer to stylized character faces. Relying on human geometric features alone to generate stylized character expressions leads to expressions that are perceptually confusing or different from the intended expression. Our framework avoids these pitfalls by learning how to transfer human facial expressions to character expressions that are both perceptually consistent and geometrically correct.

Pathologic assessment for a cancer diagnosis has for years been the “gold standard” guiding clinical care and research endeavors. However, diagnostic misclassification that can compromise clinical and research outcomes occurs in the interpretation of breast biopsy specimens, particularly at the benign to malignant and in-situ to invasive thresholds. At this point in time, technological advances are enabling widespread use of digitized whole slides. However, while this new technology has not been verified compared to glass slides it provides a much more feasible medium for evaluating visual screening behavior than a traditional microscope. For these reasons, we have designed a research program that will evaluate diagnostic accuracy on digital compared to glass slides and evaluate individuals visual screening of digital images. In-depth scientific evaluation of the visual screening behind each pathologist’s review of a slide, including characteristics of both viewing behavior and image regions they deem important, is critical to understanding the possible sources of errors and identifying the most effective viewing techniques for reducing errors. Bringing together a multi-faceted group of investigators, will move us closer to our ultimate goal of ensuring high-quality clinical care.