Abstract

The widespread adoption of whole slide imaging has increased the demand for effective and efficient gigapixel image analysis. Deep learning is at the forefront of computer vision, showcasing significant improvements over previous methodologies on visual understanding. However, whole slide images have billions of pixels and suffer from high morphological heterogeneity as well as from different types of artefacts. Collectively, these impede the conventional use of deep learning. For the clinical translation of deep learning solutions to become a reality, these challenges need to be addressed. In this paper, we review work on the interdisciplinary attempt of training deep neural networks using whole slide images, and highlight the different ideas underlying these methodologies.Recently, NHS Greater Glasgow and Clyde, one of the largest single site pathology services in Europe, has begun proceedings to undergo full digitization. As the adoption of digital pathology becomes wider, automated image analysis of tissue morphology has the potential to further establish itself in pathology and ultimately decrease the workload of pathologists, reduce turnaround times for reporting, and standardise clinical practices. For example, known or novel biomarkers and histopathological features can be automatically quantified [22,23,26,8,11,3,36,40,20]. Furthermore, deep learning techniques can be employed to recognize morphological patterns within the specimen for diagnostic and triaging purposes [38,48,32,50].

The objective of this work is to recognize all the frontal faces of a character in the closed world of a movie or situation comedy, given a small number of query faces. This is challenging because faces in a feature-length film are relatively uncontrolled with a wide variability of scale, pose, illumination, and expressions, and also may be partially occluded. We develop a recognition method based on a cascade of processing steps that normalize for the effects of the changing imaging environment. In particular there are three areas of novelty: (i) we suppress the background surrounding the face, enabling the maximum area of the face to be retained for recognition rather than a subset; (ii) we include a pose refinement step to optimize the registration between the test image and face exemplar; and (iii) we use robust distance to a sub-space to allow for partial occlusion and expression change. The method is applied and evaluated on several feature length films. It is demonstrated that high recall rates (over 92%) can be achieved whilst maintaining good precision (over 93%).

No abstract

In the context of resistance training the so-called “sticking point” is commonly understood as the position in a lift in which a disproportionately large increase in the difficulty to continue the lift is experienced. If the lift is taken to the point of momentary muscular failure, the sticking point is usually where the failure occurs. Hence the sticking point is associated with an increased chance of exercise form deterioration or breakdown. Understanding the mechanisms that lead to the occurrence of sticking points as well as different training strategies that can be used to overcome them is important to strength practitioners (trainees and coaches alike) and instrumental for the avoidance of injury and continued progress. In this article we survey and consolidate the body of existing research on the topic: we discuss different definitions of the sticking point adopted in the literature and propose a more precise definition, describe different muscular and biomechanical aspects that give rise to sticking points, and review the effectiveness of different training modalities used to address them.