Mobile Glaucoma Detection Application

Glaucoma is a debilitating optical degeneration disease that can lead to vision loss and eventually blindness. Given its asymptomatic nature, most people with Glaucoma aren’t even aware that they have the disease. As a result, the disease is often left untreated until it is too late. Detecting the presence of Glaucoma is one of the most important steps in treating Glaucoma, but is unfortunately also the most difficult to enforce. The Mobile Glaucoma Detection application aims to reduce the growing number of individuals who are unaware that they have Glaucoma by providing a simple detection mechanism to notify users if they are at risk. The system does this by enabling its users to independently conduct Tonometry exams through the application. Tonometry examinations allow doctors to determine if the intra-ocular pressure levels in a person’s eyes put them at risk for Glaucoma. The M.G.D.A(Mobile Glaucoma Detection Application) allows users to determine their intra-ocular pressure levels from the comfort of their own home via a special contact lens paired with a smartphone application. The system also offers users the opportunity to monitor, regulate, and track their use and progress through the system

Transformer-Based Visual Segmentation: A Survey

Visual segmentation seeks to partition images, video frames, or point clouds into multiple segments or groups. This technique has numerous real-world applications, such as autonomous driving, image editing, robot sensing, and medical analysis. Over the past decade, deep learning-based methods have made remarkable strides in this area. Recently, transformers, a type of neural network based on self-attention originally designed for natural language processing, have considerably surpassed previous convolutional or recurrent approaches in various vision processing tasks. Specifically, vision transformers offer robust, unified, and even simpler solutions for various segmentation tasks. This survey provides a thorough overview of transformer-based visual segmentation, summarizing recent advancements. We first review the background, encompassing problem definitions, datasets, and prior convolutional methods. Next, we summarize a meta-architecture that unifies all recent transformer-based approaches. Based on this meta-architecture, we examine various method designs, including modifications to the meta-architecture and associated applications. We also present several closely related settings, including 3D point cloud segmentation, foundation model tuning, domain-aware segmentation, efficient segmentation, and medical segmentation. Additionally, we compile and re-evaluate the reviewed methods on several well-established datasets. Finally, we identify open challenges in this field and propose directions for future research. The project page can be found at https://github.com/lxtGH/Awesome-Segmenation-With-Transformer. We will also continually monitor developments in this rapidly evolving field.Comment: Work in progress. Github: https://github.com/lxtGH/Awesome-Segmenation-With-Transforme

On the Use of XML in Medical Imaging Web-Based Applications

The rapid growth of digital technology in medical fields over recent years has increased the need for applications able to manage patient medical records, imaging data, and chart information. Web-based applications are implemented with the purpose to link digital databases, storage and transmission protocols, management of large volumes of data and security concepts, allowing the possibility to read, analyze, and even diagnose remotely from the medical center where the information was acquired. The objective of this paper is to analyze the use of the Extensible Markup Language (XML) language in web-based applications that aid in diagnosis or treatment of patients, considering how this protocol allows indexing and exchanging the huge amount of information associated with each medical case. The purpose of this paper is to point out the main advantages and drawbacks of the XML technology in order to provide key ideas for future web-based applicationsPeer ReviewedPostprint (author's final draft

Serial dependence in a simulated clinical visual search task.

In everyday life, we continuously search for and classify objects in the environment around us. This kind of visual search is extremely important when performed by radiologists in cancer image interpretation and officers in airport security screening. During these tasks, observers often examine large numbers of uncorrelated images (tumor x-rays, checkpoint x-rays, etc.) one after another. An underlying assumption of such tasks is that search and recognition are independent of our past experience. Here, we simulated a visual search task reminiscent of medical image search and found that shape classification performance was strongly impaired by recent visual experience, biasing classification errors 7% more towards the previous image content. This perceptual attraction exhibited the three main tuning characteristics of Continuity Fields: serial dependence extended over 12 seconds back in time (temporal tuning), it occurred only between similar tumor-like shapes (feature tuning), and only within a limited spatial region (spatial tuning). Taken together, these results demonstrate that serial dependence influences shape perception and occurs in visual search tasks. They also raise the possibility of a detrimental impact of serial dependence in clinical and practically relevant settings, such as medical image perception

Interpersonal Goals, Motivation, and Health-promotion Behaviors

This thesis placed third for health-promotion in the 25th Annual Denman ForumResearch shows that people's behaviors, and specifically their lack of health engagement behaviors, contributes to about 50% of all illness (Ryan 2009). Why are individuals not engaging in these health behaviors that could prevent serious illness? From the social psychological perspective, motivation is shown to be more predictive of health outcomes compared to beliefs, support, or self-efficacy (Kelly et al., 1991). Building upon the egosystem-ecosystem theory of social motivation (Crocker et al., 2017), the current research examines the association among interpersonal goals, health motivations, and health-promotion behaviors. Study 1 utilized Amazon Mechanical Turk in order to outsource surveys to 309 participants ages 22 to 70 (M = 37.78 years old). Participants completed surveys measuring interpersonal goals, health motivations, and health behaviors. Results show positive associations between both compassionate and self-image goals with motivation on health-promotion behaviors. Study 2 was an experimental design utilizing 207 participants from the student research pool at a large university (M = 19.32 years old). This study used a manipulation of compassionate and self-image goals, in which participants were assigned to either a control condition or a condition of statements designed to increase one's self-image goals or compassionate goals. The manipulation preceded the same questions from Study 1 as well as measures relating to the participant's future willingness to engage in health-promotion behaviors. The results of Study 2 replicated the correlational results from Study 1, but regression analysis showed positive associations only between compassionate goals and motivation on health-promotion behavior. The current research suggests that interpersonal motivations affect people's health behaviors and subsequent health outcomes. These results are relevant for the health field as it can aid in the understanding of how interpersonal relationships may motivate individuals to increase engagement in health-promotion behaviors.Arts and Sciences Undergraduate Research ScholarshipNo embargoAcademic Major: Psycholog