Learning scale-variant and scale-invariant features for deep image classification

Convolutional Neural Networks (CNNs) require large image corpora to be trained on classification tasks. The variation in image resolutions, sizes of objects and patterns depicted, and image scales, hampers CNN training and performance, because the task-relevant information varies over spatial scales. Previous work attempting to deal with such scale variations focused on encouraging scale-invariant CNN representations. However, scale-invariant representations are incomplete representations of images, because images contain scale-variant information as well. This paper addresses the combined development of scale-invariant and scale-variant representations. We propose a multi- scale CNN method to encourage the recognition of both types of features and evaluate it on a challenging image classification task involving task-relevant characteristics at multiple scales. The results show that our multi-scale CNN outperforms single-scale CNN. This leads to the conclusion that encouraging the combined development of a scale-invariant and scale-variant representation in CNNs is beneficial to image recognition performance

Circle-based Eye Center Localization (CECL)

We propose an improved eye center localization method based on the Hough transform, called Circle-based Eye Center Localization (CECL) that is simple, robust, and achieves accuracy on a par with typically more complex state-of-the-art methods. The CECL method relies on color and shape cues that distinguish the iris from other facial structures. The accuracy of the CECL method is demonstrated through a comparison with 15 state-of-the-art eye center localization methods against five error thresholds, as reported in the literature. The CECL method achieved an accuracy of 80.8% to 99.4% and ranked first for 2 of the 5 thresholds. It is concluded that the CECL method offers an attractive alternative to existing methods for automatic eye center localization.Comment: Published and presented at The 14th IAPR International Conference on Machine Vision Applications, 2015. http://www.mva-org.jp/mva2015

Art Authentication with Vision Transformers

In recent years, Transformers, initially developed for language, have been successfully applied to visual tasks. Vision Transformers have been shown to push the state-of-the-art in a wide range of tasks, including image classification, object detection, and semantic segmentation. While ample research has shown promising results in art attribution and art authentication tasks using Convolutional Neural Networks, this paper examines if the superiority of Vision Transformers extends to art authentication, improving, thus, the reliability of computer-based authentication of artworks. Using a carefully compiled dataset of authentic paintings by Vincent van Gogh and two contrast datasets, we compare the art authentication performances of Swin Transformers with those of EfficientNet. Using a standard contrast set containing imitations and proxies (works by painters with styles closely related to van Gogh), we find that EfficientNet achieves the best performance overall. With a contrast set that only consists of imitations, we find the Swin Transformer to be superior to EfficientNet by achieving an authentication accuracy of over 85%. These results lead us to conclude that Vision Transformers represent a strong and promising contender in art authentication, particularly in enhancing the computer-based ability to detect artistic imitations

Exploring patient satisfaction after operative and nonoperative treatment for midshaft clavicle fractures:a focus group analysis

Background: There is no consensus on the optimal treatment for displaced midshaft clavicle fractures. Several studies indicate superior patient satisfaction in favour of operative reconstruction. It is unknown what drives superior satisfaction in this treatment group. The aim of this study was to explore patient satisfaction and identify contributors to patient satisfaction after operative and nonoperative treatment for displaced midshaft clavicle fractures in adults using a focus group approach. Methods: Four face-to-face and two web-based focus groups were hosted. A total of 24 participants who were treated nonoperatively (n = 14) or operatively (n = 10) agreed to participate. Participants were selected using purposive sampling, ensuring variation in gender, age, treatment complications and outcomes. A question script was developed to systematically explore patient expectations, attitudes and satisfaction with different dimensions of care. All focus groups were voice-recorded and transcribed at verbatim. Thematic analysis was conducted on all face-to-face and web-based transcripts. Results: The main emerging themes across treatment groups were; need for more information, functional recovery, speed of recovery and patient-doctor interaction. There was no difference in themes observed between operative and nonoperative focus groups. The lack of information was the most important complaint in dissatisfied patients. Conclusion: Our study shows that informing patients about their injury, treatment options and expectations for recovery is paramount for overall patient satisfaction after treatment for a displaced midshaft clavicle fracture. Level of evidence: Level III, focus group study. </p

Vaccines as alternatives to antibiotics for food producing animals. Part 1:challenges and needs

Vaccines and other alternative products can help minimize the need for antibiotics by preventing and controlling infectious diseases in animal populations, and are central to the future success of animal agriculture. To assess scientific advancements related to alternatives to antibiotics and provide actionable strategies to support their development, the United States Department of Agriculture, with support from the World Organisation for Animal Health, organized the second International Symposium on Alternatives to Antibiotics. It focused on six key areas: vaccines; microbial-derived products; non-nutritive phytochemicals; immune-related products; chemicals, enzymes, and innovative drugs; and regulatory pathways to enable the development and licensure of alternatives to antibiotics. This article, part of a two-part series, synthesizes and expands on the expert panel discussions regarding opportunities, challenges and needs for the development of vaccines that may reduce the need for use of antibiotics in animals; new approaches and potential solutions will be discussed in part 2 of this series. Vaccines are widely used to prevent infections in food animals. Various studies have demonstrated that their animal agricultural use can lead to significant reductions in antibiotic consumption, making them promising alternatives to antibiotics. To be widely used in food producing animals, vaccines have to be safe, effective, easy to use, and cost-effective. Many current vaccines fall short in one or more of these respects. Scientific advancements may allow many of these limitations to be overcome, but progress is funding-dependent. Research will have to be prioritized to ensure scarce public resources are dedicated to areas of potentially greatest impact first, and private investments into vaccine development constantly compete with other investment opportunities. Although vaccines have the potential to improve animal health, safeguard agricultural productivity, and reduce antibiotic consumption and resulting resistance risks, targeted research and development investments and concerted efforts by all affected are needed to realize that potential