TOWARDS LONG-TERM IMPACT OF DEEP LEARNING SYSTEMS IN MEDICAL IMAGING

Deep learning has driven AI\u27s rapid growth in recent years, especially in the medical domain, where deep CNNs are the state-of-the-art for image recognition and classification. However, training them from scratch is challenging due to the lack of data and high computational requirements. Transfer Learning (TL) is an effective approach for limited training data, and TL integrated with GANs has improved image analysis models. It was unclear how much impact big data-driven Deep Learning systems had on adoption and acceptance in real-world healthcare. Specifically, the effectiveness of recently developed DL systems as scalable and generalizable AI applications remained an open question. Accordingly, the main objective of this research work is to assess the effectiveness of TL-GAN systems on broad adoption. This study explored the combination of transfer learning and generative adversarial networks (GANs) in medical imaging by conducting a systematic literature review. In addition, the scalability dimension of these systems was evaluated by examining the dynamics of GAN-augmented datasets and the accuracy achieved on target datasets. Finally, the generalization capabilities of the combination of transfer learning and GANs were evaluated. The study added to the current literature on TL and GANs in medical imaging, specifically in image synthesis and computational efficiency. Two strategies for combining TL and GANs were identified and summarized. The study also examined the impact of artificially augmented training datasets on the Fine-Tuning layer, finding that larger datasets resulted in more parameters being trained for optimal performance. Additionally, the study investigated the effect of synthetic dataset size on classification accuracy in TL settings, concluding that target validation accuracy stabilized as the dataset size increased. Furthermore, the study explored the generalizability of the models trained on GAN-augmented datasets and found that pre-trained models exhibited good performance when applied to various target datasets, indicating a high degree of generalizability in the models

Early Outlook of Public Perception on Covid-19 Booster Shots

The Covid-19 pandemic, which began in late 2019, is still ongoing. Several vaccines are available in the United States, and booster doses for all adults have been approved. However, a significant proportion of people are still afraid to get vaccinated against Covid-19. The goal of the study was to detect the emotions and topics of discussion on Twitter around the COVID-19 booster shots. The emotion and common topics were discovered using sentiment analysis and topic modeling approaches. The results suggest that the public has a negative perception of booster shots in general. Positive themes include gratitude to science for vaccines and encouraging people to get vaccinated. The negative themes concern those who are dissatisfied with the government\u27s handling of the pandemic, as well as blame the unvaccinated for the spread of the virus

Transfer Learning in Medical Image Classification: Challenges and Opportunities

Transfer Learning is currently popular in Medical Image classification. Transfer Learning methods are extensively applied with CNN’s such as Res-net, Densenet, VGG16, Inception, etc. for various medical diagnosis tasks. CNN’s are around since the 1980s, but 60-80 percent of the TL research in MIC is done in the last three years. While CNN’s can be traditionally used as they are, they have been ensembled, segmented and improvised in recent days to resolve multiple MIC problems. This Review identified three main challenges in implementing Transfer Learning for Medical Image Classification (1) Overparameterization of deep CNN’s (2) Expensive Computations and (3) Insufficient availability of labeled data in the Medical field. The study also identified the opportunities in the form of Light-weight architectures and Multi-stage Transfer Learning which could potentially mitigate the above-mentioned challenges

Strategic Use of Social Media by Higher Education to Influence Alumni Engagement

Social media is a low-cost platform to develop and maintain communications and relationships while overcoming the barrier of distance. Higher education is an expensive affair especially with financial needs growing day by day, opening up the need for alumni to involve in fundraising and institutional development. Higher education institution’s fundraising initiatives will need to adopt social media to establish and maintain ties with alumni. This literature review study investigates the strategic use of social media by higher education to influence alumni engagement. Articles from the Web of Science and Proquest are included in this review with a focus on how institutions employ social media to engage alumni and alumni engagement behaviors towards the alma mater. Our results suggest that age, program delivery method and academic program of study are significant factors to determine the donative behaviors of alumni

Multi-Stage Transfer Learning System with Light-weight Architectures in Medical Image Classification

Transfer Learning methods are extensively applied with standard CNN architectures for various medical diagnoses. However, these architectures are computationally expensive, tend to be over parameterized, and requires a relatively large labeled datasets which are often not available in the medical image domain. Accordingly, this paper proposes a Multi-Stage Transfer Learning System using lightweight architectures to address problems with limited data and to improve training time. Preliminary results suggest that our model performed well on CT Head images over traditional single-stage transfer learning

A Meta-Analysis of Evolution of Deep Learning Research in Medical Image Analysis

With a text mining and bibliometrics approach, we review the literature on the evolution of deep learning in medical image literature from 2012 – 2020 in order to understand the current state of the research and to identify the major research themes in image analysis to answer our research questions: RQ1: What are the learning modes that are evident in the literature? RQ2: What are the emerging learning modes in the literature? RQ3: What are the major themes in medical imaging literature? The analysis of 8704 resulting from a data collection process from peer-reviewed databases, our analysis discovered the six major themes of image segmentation studies, studies with image classification, evaluation procedures such as sensitivity and specificity, optical coherence tomography studies, MRI imaging studies, and Chest imaging studies. Additionally, we assessed the number of articles published each year, the frequent keywords, the author networks, the trending topics, and connections to other topics. We discovered that segmenting and classifying the images are the most common tasks. Transfer learning is the most researched area and cancer is the highly targeted disease and Covid-19 is the most recent research tren

Agile Project Management: A Systematic Literature Review of Adoption Drivers and Critical Success Factors

With an emphasis on adaptive processes that respond to uncertainties, the Agile Project Management (APM) approach has evolved the way projects are managed beyond the traditional processes. This study aims to investigate recent literature on APM to discover the adoption drivers and the critical success factors that influence APM success and provide recommendations for the development of APM best practices. The study conducted a literature search on academic databases ABI/Inform, ACM Digital Library, EBSCO Host, and IEEE Xplore with keywords ‘agile’ and ‘project management’ for peer-reviewed English language articles published between January 2015 and January 2020 to discover insights regarding adoption drivers and critical success factors. Nine (9) drivers of adoption and thirteen (13) critical success factors related to the project dimensions of Project, Team, and Culture. The findings of this study outline the current state of APM adoption and use and is relevant to project management practitioners and researchers

Early Outlook of Public Perception on Covid-19 Booster Shots

The Covid-19 pandemic, which began in late 2019, is still ongoing. Several vaccines are available in the United States, and booster doses for all adults have been approved. However, a significant proportion of people are still afraid to get vaccinated against Covid-19. The goal of the study was to detect the emotions and topics of discussion on Twitter around the COVID-19 booster shots. The emotion and common topics were discovered using sentiment analysis and topic modeling approaches. The results suggest that the public has a negative perception of booster shots in general. Positive themes include gratitude to science for vaccines and encouraging people to get vaccinated. The negative themes concern those who are dissatisfied with the government\u27s handling of the pandemic, as well as blame the unvaccinated for the spread of the virus