Examining the Lived Experience of Student Veterans Using Photovoice Methodology

The primary objective of this study was to understand the lived experience of student veterans using photovoice methodology. After returning from service veterans struggle most with school (Plach & Haertlein Sells, 2013). Student veterans experience difficulty in time management, and transitioning to student life (Radford, 2009). They spend more time working at jobs and caring for dependents than non-veteran students, but spend equivalent time studying. They perceive less engagement with faculty and campus support than their peers (NSSE, 2010). For many veterans, education is a primary occupation but there is dearth of data about their lived experience and factors that help or hinder their pursuit of educational goals. This study employed a qualitative research design using photovoice methodology (Wang & Burris, 1997) to gain such insight. Student veterans were recruited after obtaining Institutional Review Board approval and informed consent. After being trained in the photovoice methodology, participants were provided with cameras to capture aspects of their life that they wanted to convey regarding the transition process through photography. With their photographs as references, participants engaged in group discussions (audio recorded), and wrote narratives that they consider important to convey to multiple audiences including researchers, health promotion providers, university personnel and policy makers. These narratives conveyed lived experiences that reflect challenges experienced while attaining an education and factors that helped them to overcome such challenges. Narratives and discussion session transcripts were analyzed using thematic analysis and descriptive coding. Analysis lead to formulation of four themes: 1) reminiscence of past duty and reflections on military life, 2) transition from military to civilian student life, and 3) entry to a new stage of life and 4) university and community environment. Findings from this study can help researchers, health promotion providers, the higher education community, and policy makers to acknowledge the factors that challenge or support student veterans so that programs and services can be offered to assist them in attaining their educational goals

An ethnographic analysis of stigma towards mental illness and mental health care at Clubhouses in North Carolina

The purpose of this dissertation study was to identify the social processes guiding the experiences of stigma and occupational engagement (mental healthcare and community participation) for adults with serious mental illness. I employed an ethnographic approach to conduct this study. Aligned with the ethnographic approach, methods including interviews, fieldwork/participant observation, and document review were employed to collect data at two clubhouses in North Carolina. A total of eighteen adults with serious mental illness and sixteen clubhouse staff or service providers participated and their perspectives on the topics of interest, such as stigma and mental healthcare, were collected over a period of six months. Additionally, seven policy experts were interviewed to gather their perspectives on the influence of stigma on mental healthcare policies. Data were analyzed using open and focused coding along with analytic interpretation. The analysis led to generation of three papers that illustrate: 1) a social process (titled moral economics of occupations framework) conceptualizing occupations as assets and their relevance in maintaining institutional practices; 2) a conceptual framework highlighting the relationship between stigma, community participation, and mental healthcare policies; and 3) a social process (titled principle of gradient rationality) guiding experiences of stigma on an interactional level. Future research is required to assess validity and applicability of the proposed frameworks in different settings. Further, in order to address structural/institutional stigma, future research regarding marginalizing policies is required, as many adults with serious mental illness continue to struggle due to systemic issues, such as incarceration, unemployment, poverty, and homelessness.Doctor of Philosoph

TransRUPNet for Improved Out-of-Distribution Generalization in Polyp Segmentation

Out-of-distribution (OOD) generalization is a critical challenge in deep learning. It is specifically important when the test samples are drawn from a different distribution than the training data. We develop a novel real-time deep learning based architecture, TransRUPNet that is based on a Transformer and residual upsampling network for colorectal polyp segmentation to improve OOD generalization. The proposed architecture, TransRUPNet, is an encoder-decoder network that consists of three encoder blocks, three decoder blocks, and some additional upsampling blocks at the end of the network. With the image size of $256\times256$, the proposed method achieves an excellent real-time operation speed of \textbf{47.07} frames per second with an average mean dice coefficient score of 0.7786 and mean Intersection over Union of 0.7210 on the out-of-distribution polyp datasets. The results on the publicly available PolypGen dataset (OOD dataset in our case) suggest that TransRUPNet can give real-time feedback while retaining high accuracy for in-distribution dataset. Furthermore, we demonstrate the generalizability of the proposed method by showing that it significantly improves performance on OOD datasets compared to the existing methods

RUPNet: Residual upsampling network for real-time polyp segmentation

Colorectal cancer is among the most prevalent cause of cancer-related mortality worldwide. Detection and removal of polyps at an early stage can help reduce mortality and even help in spreading over adjacent organs. Early polyp detection could save the lives of millions of patients over the world as well as reduce the clinical burden. However, the detection polyp rate varies significantly among endoscopists. There is numerous deep learning-based method proposed, however, most of the studies improve accuracy. Here, we propose a novel architecture, Residual Upsampling Network (RUPNet) for colon polyp segmentation that can process in real-time and show high recall and precision. The proposed architecture, RUPNet, is an encoder-decoder network that consists of three encoders, three decoder blocks, and some additional upsampling blocks at the end of the network. With an image size of $512 \times 512$, the proposed method achieves an excellent real-time operation speed of 152.60 frames per second with an average dice coefficient of 0.7658, mean intersection of union of 0.6553, sensitivity of 0.8049, precision of 0.7995, and F2-score of 0.9361. The results suggest that RUPNet can give real-time feedback while retaining high accuracy indicating a good benchmark for early polyp detection.Comment: Accepted SPIE Medical Imaging 202

Prevalence of vitamin D deficiency and its relationship with thyroid autoimmunity in Asian Indians: a community-based survey

25-Hydroxy vitamin D (25(OH)D) deficiency is linked with predisposition to autoimmune type 1 diabetes and multiple sclerosis. Our objective was to assess the relationship between serum 25(OH)D levels and thyroid autoimmunity. Subjects included students, teachers and staff aged 16-60 years (total 642, 244 males, 398 females). Serum free thyroxine, thyroid-stimulating hormone (TSH), and thyroid peroxidase autoantibodies (TPOAb), intact parathyroid hormone and 25(OH)D were measured by electrochemiluminescence and RIA, respectively. Thyroid dysfunction was defined if (1) serum TSH ≥ 5 μ U/ml and TPOAb>34 IU/ml or (2) TSH ≥ 10 μ U/ml but normal TPOAb. The mean serum 25(OH)D of the study subjects was 17.5 (SD 10.2) nmol/l with 87 % having values ≤ 25 nmol/l. TPOAb positivity was observed in 21 % of subjects. The relationship between 25(OH)D and TPOAb was assessed with and without controlling for age and showed significant inverse correlation (r - 0.08, P = 0.04) when adjusted for age. The prevalence of TPOAb and thyroid dysfunction were comparable between subjects stratified according to serum 25(OH)D into two groups either at cut-off of ≤ 25 or >25 nmol/l or first and second tertiles. Serum 25(OH)D values show only weak inverse correlation with TPOAb titres. The presence of such weak association and narrow range of serum 25(OH)D did not allow us to interpret the present results in terms of quantitative cut-off values of serum 25(OH)D. Further studies in vitamin D-sufficient populations with wider range of serum 25(OH)D levels are required to substantiate the findings of the current study

Adaptive Smooth Activation for Improved Disease Diagnosis and Organ Segmentation from Radiology Scans

In this study, we propose a new activation function, called Adaptive Smooth Activation Unit (ASAU), tailored for optimized gradient propagation, thereby enhancing the proficiency of convolutional networks in medical image analysis. We apply this new activation function to two important and commonly used general tasks in medical image analysis: automatic disease diagnosis and organ segmentation in CT and MRI. Our rigorous evaluation on the RadImageNet abdominal/pelvis (CT and MRI) dataset and Liver Tumor Segmentation Benchmark (LiTS) 2017 demonstrates that our ASAU-integrated frameworks not only achieve a substantial (4.80\%) improvement over ReLU in classification accuracy (disease detection) on abdominal CT and MRI but also achieves 1\%-3\% improvement in dice coefficient compared to widely used activations for `healthy liver tissue' segmentation. These improvements offer new baselines for developing a diagnostic tool, particularly for complex, challenging pathologies. The superior performance and adaptability of ASAU highlight its potential for integration into a wide range of image classification and segmentation tasks

GastroVision: A Multi-class Endoscopy Image Dataset for Computer Aided Gastrointestinal Disease Detection

Integrating real-time artificial intelligence (AI) systems in clinical practices faces challenges such as scalability and acceptance. These challenges include data availability, biased outcomes, data quality, lack of transparency, and underperformance on unseen datasets from different distributions. The scarcity of large-scale, precisely labeled, and diverse datasets are the major challenge for clinical integration. This scarcity is also due to the legal restrictions and extensive manual efforts required for accurate annotations from clinicians. To address these challenges, we present \textit{GastroVision}, a multi-center open-access gastrointestinal (GI) endoscopy dataset that includes different anatomical landmarks, pathological abnormalities, polyp removal cases and normal findings (a total of 27 classes) from the GI tract. The dataset comprises 8,000 images acquired from B{\ae}rum Hospital in Norway and Karolinska University Hospital in Sweden and was annotated and verified by experienced GI endoscopists. Furthermore, we validate the significance of our dataset with extensive benchmarking based on the popular deep learning based baseline models. We believe our dataset can facilitate the development of AI-based algorithms for GI disease detection and classification. Our dataset is available at \url{https://osf.io/84e7f/}

ConvSegNet: Automated Polyp Segmentation From Colonoscopy Using Context Feature Refinement With Multiple Convolutional Kernel Sizes

Colorectal cancer occurs in the rectal of humans, and early detection has been proved to reduce its mortality rate. Colonoscopy is the standard used in detecting the presence of polyps in the rectal, and accurate segmentation of the polyps from colonoscopy images often provides helpful information for early diagnosis and treatment. Although existing deep learning models often achieve high segmentation performance when tested on the same dataset used in model training; still, their performance often degrades when applied to out-of-distribution datasets, leading to low model generalization or overfitting. This challenge is often associated with the quality of the features learnt from the input images. In this work, a novel Context Feature Refinement (CFR) module is proposed to address the challenge of low model generalization and segmentation performance. The CFR module is built to extract contextual information from the incoming feature map by using multiple parallel convolutional layers with progressively increasing kernel sizes. Using multiple parallel convolutions with different kernel sizes helped to extract more efficient multi-scale contextual information and thus enabled the network to effectively identify and segment small and fine details, as well as larger and more complex structures in the input images. Extensive experiments on three public benchmark datasets in CVC-ClinicDB, Kvasir-SEG, and BKAI-NeoPolyp showed that the proposed ConvSegNet model achieved jaccard, dice and F2 scores of 0.8650, 0.9177, and 0.9328 on CVC-ClinicDB, 0.7936, 0.8618, and 0.8855 on Kvasir-SEG, and 0.8045, 0.8747 and 0.8909 on BKAI-NeoPolyp datasets respectively. Also, an improved generalization performance was achieved by the ConvSegNet model, compared to the benchmark polyp segmentation models. Code is available at https://github.com/AOige/ConvSegNet

Statewide mental health training for probation officers: improving knowledge and decreasing stigma

Abstract Background The large and growing number of probationers with mental illnesses pose significant challenges to the probationer officers who supervise them. Stigma towards mental illnesses among probation officers is largely unstudied and the effectiveness of training initiatives designed to educate probation officers about mental illness is unknown. To address these gaps in the literature, we report findings from a statewide mental health training initiative designed to improve probation officers’ knowledge of mental illnesses. A single-group pretest posttest design was used and data about stigma towards mental illnesses and knowledge of mental illnesses were collected from 316 probation officers. Data were collected prior to and shortly after officers viewed a series of educational training modules about mental illnesses. Results Officers’ knowledge of mental illnesses increased and officers demonstrated lower levels of stigma towards persons with mental illnesses as evidenced by scores on a standardized scale. Conclusion Mental health education can help decrease stigma and increase knowledge of mental illnesses among probation officers. More research is needed to assess the impact of these trainings on probationers’ mental health and criminal justice outcomes