402 research outputs found

    Towards Neuromorphic Gradient Descent: Exact Gradients and Low-Variance Online Estimates for Spiking Neural Networks

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    Spiking Neural Networks (SNNs) are biologically-plausible models that can run on low-powered non-Von Neumann neuromorphic hardware, positioning them as promising alternatives to conventional Deep Neural Networks (DNNs) for energy-efficient edge computing and robotics. Over the past few years, the Gradient Descent (GD) and Error Backpropagation (BP) algorithms used in DNNs have inspired various training methods for SNNs. However, the non-local and the reverse nature of BP, combined with the inherent non-differentiability of spikes, represent fundamental obstacles to computing gradients with SNNs directly on neuromorphic hardware. Therefore, novel approaches are required to overcome the limitations of GD and BP and enable online gradient computation on neuromorphic hardware. In this thesis, I address the limitations of GD and BP with SNNs by proposing three algorithms. First, I extend a recent method that computes exact gradients with temporally-coded SNNs by relaxing the firing constraint of temporal coding and allowing multiple spikes per neuron. My proposed method generalizes the computation of exact gradients with SNNs and enhances the tradeoffs between performance and various other aspects of spiking neurons. Next, I introduce a novel alternative to BP that computes low-variance gradient estimates in a local and online manner. Compared to other alternatives to BP, the proposed method demonstrates an improved convergence rate and increased performance with DNNs. Finally, I combine these two methods and propose an algorithm that estimates gradients with SNNs in a manner that is compatible with the constraints of neuromorphic hardware. My empirical results demonstrate the effectiveness of the resulting algorithm in training SNNs without performing BP

    Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives

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    Deep learning has demonstrated remarkable performance across various tasks in medical imaging. However, these approaches primarily focus on supervised learning, assuming that the training and testing data are drawn from the same distribution. Unfortunately, this assumption may not always hold true in practice. To address these issues, unsupervised domain adaptation (UDA) techniques have been developed to transfer knowledge from a labeled domain to a related but unlabeled domain. In recent years, significant advancements have been made in UDA, resulting in a wide range of methodologies, including feature alignment, image translation, self-supervision, and disentangled representation methods, among others. In this paper, we provide a comprehensive literature review of recent deep UDA approaches in medical imaging from a technical perspective. Specifically, we categorize current UDA research in medical imaging into six groups and further divide them into finer subcategories based on the different tasks they perform. We also discuss the respective datasets used in the studies to assess the divergence between the different domains. Finally, we discuss emerging areas and provide insights and discussions on future research directions to conclude this survey.Comment: Under Revie

    Can Deep Learning Reliably Recognize Abnormality Patterns on Chest X-rays? A Multi-Reader Study Examining One Month of AI Implementation in Everyday Radiology Clinical Practice

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    In this study, we developed a deep-learning-based automatic detection algorithm (DLAD, Carebot AI CXR) to detect and localize seven specific radiological findings (atelectasis (ATE), consolidation (CON), pleural effusion (EFF), pulmonary lesion (LES), subcutaneous emphysema (SCE), cardiomegaly (CMG), pneumothorax (PNO)) on chest X-rays (CXR). We collected 956 CXRs and compared the performance of the DLAD with that of six individual radiologists who assessed the images in a hospital setting. The proposed DLAD achieved high sensitivity (ATE 1.000 (0.624-1.000), CON 0.864 (0.671-0.956), EFF 0.953 (0.887-0.983), LES 0.905 (0.715-0.978), SCE 1.000 (0.366-1.000), CMG 0.837 (0.711-0.917), PNO 0.875 (0.538-0.986)), even when compared to the radiologists (LOWEST: ATE 0.000 (0.000-0.376), CON 0.182 (0.070-0.382), EFF 0.400 (0.302-0.506), LES 0.238 (0.103-0.448), SCE 0.000 (0.000-0.634), CMG 0.347 (0.228-0.486), PNO 0.375 (0.134-0.691), HIGHEST: ATE 1.000 (0.624-1.000), CON 0.864 (0.671-0.956), EFF 0.953 (0.887-0.983), LES 0.667 (0.456-0.830), SCE 1.000 (0.366-1.000), CMG 0.980 (0.896-0.999), PNO 0.875 (0.538-0.986)). The findings of the study demonstrate that the suggested DLAD holds potential for integration into everyday clinical practice as a decision support system, effectively mitigating the false negative rate associated with junior and intermediate radiologists

    The Use of Strategic Public Relations Communication Techniques in Campaigns to Raise Awareness of Breast Cancer: A Case Study of Breast Cancer Campaigns in Saudi Arabian Charities

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    This study investigates the efforts of cancer charities in the Kingdom of Saudi Arabia to raise awareness of breast cancer through communication campaign techniques in order to reduce its incidence, which has been rising in the Saudi population for several years. Applying the Diffusion of Innovations Theory of Rogers (2003) as a theoretical framework, qualitative primary data was collected through semi-structured interviews with 12 individuals working in public relations (PR) and communications practice at six cancer charities to understand their experience of designing and planning health communication strategies to bring about health-related behavioural change among Saudi women. The study also involved qualitative content analysis of the Twitter pages of the six charities during Breast Cancer Awareness Month (October) in 2018 to determine communicative functions in accordance with the classification scheme of Lovejoy and Saxton (2012). The interview data revealed that not all of the charities employed dedicated PR practitioners in their communication departments, but all carried out some PR functions, with a significant emphasis on the technical rather than managerial roles of PR. The participants were found to use various communication strategies and methods to reach different target audiences. However, considerable difficulty was experienced in the design of specific campaign planning strategies, with the participants demonstrating little use of breast cancer campaign strategy to overcome the lack of knowledge and awareness among Saudi women. The study confirmed that the charities did not use Twitter strategically, employing the platform largely as a one-way channel of information communication. Additionally, the charities rarely used promotional and mobilising messages as an action function and did not follow the commonly accepted relationship-building strategies such as dialogic and two-way communication

    Computerized Clinical Decision Support Systems for decision support in patients with breast, lung, colorectal or prostate cancer

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    Sistemes electrònics; Càncer; Presa de decisionsSistemas electrónicos; Cáncer; Toma de decisionesElectronic systems; Cancer; Decision makingEl objetivo general de este informe de ETS es evaluar la seguridad, eficacia, efectividad y eficiencia de los sistemas electrónicos de apoyo a las decisiones clínicas (computerized Clinical Decision Support Systems o cCDSS), específicamente de los considerados de nivel medio (p. ej. calculadoras pronósticas o GPC automatizadas) y de nivel alto (aquellos que utilizan la IA para formular recomendaciones específicas para un paciente), para el apoyo a la toma de decisiones clínicas relativas al manejo terapéutico, seguimiento o pronóstico de pacientes con cáncer de mama, pulmón, colon-recto o próstata. También se propone evaluar el impacto de los cCDSS en cáncer a nivel organizativo, legal, ético y social/de pacientes.L'objectiu general d'aquest informe d'ETS és avaluar la seguretat, eficàcia, efectivitat i eficiència dels sistemes electrònics de suport a les decisions clíniques (computeritzed Clinical Decision Support Systems o cCDSS), específicament dels considerats de nivell mitjà (p. ex. calculadores pronòstiques o GPC automatitzades) i de nivell alt (aquells que utilitzen la IA per formular recomanacions específiques per a un pacient), per al suport a la presa de decisions clíniques relatives al maneig terapèutic, seguiment o pronòstic de pacients amb càncer de mama, pulmó, còlon-recte o pròstata. També es proposa avaluar l'impacte dels cCDSS en càncer a nivell organitzatiu, legal, ètic i social/de pacients.The overall objective of this HTA report is to evaluate the safety, efficacy, effectiveness, and efficiency of (computeritzed Clinical Decision Support Systems (cCDSS), specifically those considered medium level (e.g. prognostic calculators or automated CPGs) and high level (those that use AI to formulate patient-specific recommendations), for clinical decision support regarding the therapeutic management, follow-up, or prognosis of patients with breast, lung, colon-rectum or prostate cancer. It is also proposed to assess the impact of cCDSS in cancer at organizational, legal, ethical, and social/patient level

    Detección Asistida por Ordenador basada en redes neuronales de convolución en tomografía computarizada y mamografía: diseño de sistemas, desarrollo de la aplicación JORCAD y validación en un contexto educativo

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    [ES]El Radiodiagnóstico es una especialidad médica que ha vivido un rápido desarrollo tecnológico en las últimas décadas, convirtiéndose en una herramienta diagnóstica de primer nivel en Medicina. La IA ha supuesto una revolución en muchas áreas del conocimiento, incluyendo el radiodiagnóstico, donde su irrupción como sistemas de soporte en la toma de decisiones de los especialistas ha supuesto un cambio de paradigma en la práctica clínica. Estos sistemas han demostrado su utilidad en tareas como la detección de lesiones y su clasificación o diagnóstico. Sin embargo, su gran potencial como herramientas que asistan en diferentes etapas del proceso de aprendizaje de estudiantes de medicina y residentes, parece haber quedado en segundo plano con respecto a las aplicaciones clínicas. El interés en la imagen radiológica y en ambas vertientes de la IA dota a esta Tesis Doctoral de un carácter interdisciplinar, al estar relacionada con la informática mediante el desarrollo de un sistema de IA, la radiología y la física médica a través del uso de imágenes de dos modalidades radiológicas para la detección de lesiones, siendo necesario su tratamiento y procesado, y también con la educación mediante el desarrollo de una aplicación educativa para la formación de especialistas en radiodiagnóstico (JORCAD) y la realización de una actividad formativa interactiva para su validación

    Comparative Analysis of Segment Anything Model and U-Net for Breast Tumor Detection in Ultrasound and Mammography Images

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    In this study, the main objective is to develop an algorithm capable of identifying and delineating tumor regions in breast ultrasound (BUS) and mammographic images. The technique employs two advanced deep learning architectures, namely U-Net and pretrained SAM, for tumor segmentation. The U-Net model is specifically designed for medical image segmentation and leverages its deep convolutional neural network framework to extract meaningful features from input images. On the other hand, the pretrained SAM architecture incorporates a mechanism to capture spatial dependencies and generate segmentation results. Evaluation is conducted on a diverse dataset containing annotated tumor regions in BUS and mammographic images, covering both benign and malignant tumors. This dataset enables a comprehensive assessment of the algorithm's performance across different tumor types. Results demonstrate that the U-Net model outperforms the pretrained SAM architecture in accurately identifying and segmenting tumor regions in both BUS and mammographic images. The U-Net exhibits superior performance in challenging cases involving irregular shapes, indistinct boundaries, and high tumor heterogeneity. In contrast, the pretrained SAM architecture exhibits limitations in accurately identifying tumor areas, particularly for malignant tumors and objects with weak boundaries or complex shapes. These findings highlight the importance of selecting appropriate deep learning architectures tailored for medical image segmentation. The U-Net model showcases its potential as a robust and accurate tool for tumor detection, while the pretrained SAM architecture suggests the need for further improvements to enhance segmentation performance

    Artificial Intelligence-Based Medical Device Technologies Implementation Strategies in the Nigerian Health Care Industry

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    Artificial intelligence (AI)-based medical device technologies can aid medical professionals in delivering faster and more accurate treatment, but health care leaders are concerned with eliminating challenges that impede implementation. Grounded in the technology-organization-environment and technology acceptance models, the purpose of this qualitative multi-case study was to explore strategies health care leaders in Nigeria use to obtain, adopt, and implement AI-based medical device technologies. The participants were 11 health care leaders in Nigeria who successfully implemented AI-based medical device technologies in their hospitals. Data were collected using semi-structured interviews and the review of organizational documents. Through thematic analysis, five themes were identified: (a) implementation strategies, (b) barriers to implementation, (c) factors influencing the adoption of the technologies, (d) improvement in the health care system, and (e) infrastructure and equipment. A key recommendation is for healthcare leaders to ensure financing is in place before any meaningful advanced medical device projects could be accomplished. The implications for positive social change include the potential to provide the communities with enhanced care using the monitoring and predictive features of AI-based medical devices and improving patient-centered health quality care

    Costs of quality assurance in the German medical market.

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    The central subject of this dissertation, combined with a methodological study, is the economic analysis of Quality Assurance in the German health care system. With the help of the study an estimate of the total costs in the German health care market shall be given. The focus of the analysis is on Companies, Political Bodies, Liberal Professions and Public Corporations which are part of the health care system including Certification Bodies, which lead to costs from using Quality Assurance and interacting with the health care system. First, a systematic literature search was conducted to determine the costs. It was found that there are no articles or publications that address the topic of total cost of quality assurance. A continuation/update of existing studies was therefore not possible. To be able to estimate the total costs of Quality Assurance in the German health care market, the Quality Assurance costs were surveyed using a bottom-up analysis. After identifying organizations and collecting relevant data, the total costs of quality assurance in the German healthcare market were estimated using a mathematical calculation. In general, ensuring quality is an original part of the actions of all professional groups and institutions working in the health care system. Due to this importance, it is remarkable that an economic analysis of the total costs has never taken place before. One reason for this may be the "complexity of the German health care system". Furthermore, the costs of quality assurance are not listed separately, but as part of general administrative expenses. Controlling and transparent presentation of the costs is therefore not possible. The cost estimation and the database created for this study about the parties involved in quality assurance in the German health care market can be a useful support for further studies in this field of research.Medicin
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