An objective validation of polyp and instrument segmentation methods in colonoscopy through Medico 2020 polyp segmentation and MedAI 2021 transparency challenges

Automatic analysis of colonoscopy images has been an active field of research motivated by the importance of early detection of precancerous polyps. However, detecting polyps during the live examination can be challenging due to various factors such as variation of skills and experience among the endoscopists, lack of attentiveness, and fatigue leading to a high polyp miss-rate. Deep learning has emerged as a promising solution to this challenge as it can assist endoscopists in detecting and classifying overlooked polyps and abnormalities in real time. In addition to the algorithm's accuracy, transparency and interpretability are crucial to explaining the whys and hows of the algorithm's prediction. Further, most algorithms are developed in private data, closed source, or proprietary software, and methods lack reproducibility. Therefore, to promote the development of efficient and transparent methods, we have organized the "Medico automatic polyp segmentation (Medico 2020)" and "MedAI: Transparency in Medical Image Segmentation (MedAI 2021)" competitions. We present a comprehensive summary and analyze each contribution, highlight the strength of the best-performing methods, and discuss the possibility of clinical translations of such methods into the clinic. For the transparency task, a multi-disciplinary team, including expert gastroenterologists, accessed each submission and evaluated the team based on open-source practices, failure case analysis, ablation studies, usability and understandability of evaluations to gain a deeper understanding of the models' credibility for clinical deployment. Through the comprehensive analysis of the challenge, we not only highlight the advancements in polyp and surgical instrument segmentation but also encourage qualitative evaluation for building more transparent and understandable AI-based colonoscopy systems

User Authentication in Cloud Computing- Using Seed Chain Based One Time Password (OTP)

Abstract-Cloud computing has emerged as a popular model in computing world to support processing large volumetric data using clusters of commodity computers. It is the latest effort in delivering computing resources as a service. It is used to describe both a platform and a type of application, therefore removing the need of providing these services themselves. This can for example lead to cost savings, better resource utilization and removing the need of technical expertise for the customers. However, cloud services also present a couple of issues. Since the resources are put under another provider, the customer will have no control over the situation. Since the control of services and data needed for the everyday-run of a corporation is being handled by another company, further issues needs to be concerned. The consumer needs to trust the provider, and know that they handle their data in a correct manner, and that resources can be accessed when needed. This thesis focuses on authentication in cloud services. The current solutions used today to login to cloud services have been investigated and concluded that they don't satisfy the needs for cloud services. They are either insecure or complex. This thesis have resulted in an authentication and registration method that is both secure and easy to use, therefore fulfilling the needs of cloud service authentication. The conclusions that can be drawn is that the proposed security solution in this thesis work functions very well, and provide good security together with an ease of use for the clients who don't have so much technical knowledge

A numerical study on the effect of initial void ratio and areal extent of heterogeneity on instability onset in granular media

by Debayan Bhattacharya and Amit Prashan

Mesh size sensitivity and effect of perturbation intensity on coupled undrained instability analysis in sands

by Debayan Bhattacharya and Amit Prashan

A study of the impact of psychiatric distress on coping responses and the levels of anxiety, depression, and suicidal ideation among undergraduate nursing students

Background: Entering medical professional courses is stressful to students in general and more so in a profession like nursing. Identifying the most effective methods of coping strategies with psychiatric distress may help to reduce mental health issues. This study was conducted to estimate the levels of psychiatric distress, anxiety, depression, and suicidal ideation, and their correlation with coping responses among undergraduate nursing students. Methodology and Participants: Sixty-eight 1st-year BSC nursing students participated in the study. The students were surveyed online using Google Forms. Participants had to fill up five scales: General Health Questionnaire (GHQ-28); Coping response inventory; State-Trait Anxiety Inventory (STAI); Beck Depression Inventory (BDI); and Adult Suicidal Ideation Questionnaire (ASIQ). Results: Thirty-nine students (57.35%) were found to be facing psychiatric distress (GHQ-28 Score >4). Sixteen (23.5%) had moderate depression and 13 (19.1%) had severe depression scores. Psychiatric distress scores were positively correlated with Depression, Anxiety, and Suicidal Ideation scores. A positive correlation was also found between suicidal ideation and anxiety and depression. A significant positive correlation was found between emotional discharge and acceptance/resignation (coping strategies) and GHQ, STAI, BDI, and ASIQ Scores. Positive reappraisal (PR), seeking guidance and support (SG), and problem-solving were the three coping strategies that had negative correlations with psychiatric distress, state anxiety, and suicidal ideation. Conclusion: Psychiatric distress increased depression levels, anxiety as well as suicidal ideation. Emotional discharge was identified as the most commonly used coping response. PR, SG, and problem-solving were the three most effective coping strategies which helped in reducing perceived stress levels

Stress-state dependency of small-strain shear modulus in silty sand and sandy silt of Ganga

This study explores the effect of stress state on small strain modulus (Gmax) in the naturally occurring Ganga sand and silt using bender elements during a series of carefully performed undrained triaxial tests. Unlike the previous studies with drained shearing or testing of dry samples, the undrained shearing is chosen here to decouple the effects of void ratio and stress state on the small strain stiffness. The results from undrained shearing of anisotropically and isotropically consolidated medium dense sand specimens are compared by keeping the same relative density and matching the initial mean effective pressure. The measured Gmax values show stress path dependency, i.e., it is a function of both mean effective stress and deviatoric stress. This behaviour of Gmax is captured well by a nonlinear isotropic model following energy conservation principle, which incorporates the effect of both mean effective stress and deviatoric stress. The stress strain response of soil shows some interesting observations such as the strain hardening occurs mostly at constant effective stress ratio and the stress ratio at phase transition varies with the effective confinement. Experiments with varying silt content in the sand indicate that higher fines content results into slightly lower Gmax values mainly due to the lower values of mean effective stress and deviatoric stress during undrained shearing.Amit Prashant, Debayan Bhattacharya and S. Gundlapall