The Artificial Intelligence in Digital Pathology and Digital Radiology: Where Are We?

This book is a reprint of the Special Issue entitled "The Artificial Intelligence in Digital Pathology and Digital Radiology: Where Are We?". Artificial intelligence is extending into the world of both digital radiology and digital pathology, and involves many scholars in the areas of biomedicine, technology, and bioethics. There is a particular need for scholars to focus on both the innovations in this field and the problems hampering integration into a robust and effective process in stable health care models in the health domain. Many professionals involved in these fields of digital health were encouraged to contribute with their experiences. This book contains contributions from various experts across different fields. Aspects of the integration in the health domain have been faced. Particular space was dedicated to overviewing the challenges, opportunities, and problems in both radiology and pathology. Clinal deepens are available in cardiology, the hystopathology of breast cancer, and colonoscopy. Dedicated studies were based on surveys which investigated students and insiders, opinions, attitudes, and self-perception on the integration of artificial intelligence in this field

A Research on the Classification and Applicability of the Mobile Health Applications

Mobile health applications are applied for different purposes. Healthcare professionals and other users can use this type of mobile applications for specific tasks, such as diagnosis, information, prevention, treatment, and communication. This paper presents an analysis of mobile health applications used by healthcare professionals and their patients. A secondary objective of this article is to evaluate the scientific validation of these mobile health applications and to verify if the results provided by these applications have an underlying sound scientific foundation. This study also analyzed literature references and the use of mobile health applications available in online application stores. In general, a large part of these mobile health applications provides information about scientific validation. However, some mobile health applications are not validated. Therefore, the main contribution of this paper is to provide a comprehensive analysis of the usability and user-perceived quality of mobile health applications and the challenges related to scientific validation of these mobile applications.This work was funded by FCT/MCTES through national funds and when applicable co-funded EU funds under the project UIDB/EEA/50008/2020 (Este trabalho é financiado pela FCT/MCTES através de fundos nacionais e quando aplicável cofinanciado por fundos comunitários no âmbito do projeto UIDB/EEA/50008/2020)

cii Student Papers - 2021

In this collection of papers, we, the Research Group Critical Information Infrastructures (cii) from the Karlsruhe Institute of Technology, present nine selected student research articles contributing to the design, development, and evaluation of critical information infrastructures. During our courses, students mostly work in groups and deal with problems and issues related to sociotechnical challenges in the realm of (critical) information systems. Student papers came from four different cii courses, namely Emerging Trends in Digital Health, Emerging Trends in Internet Technologies, Critical Information Infrastructures, and Digital Health in the winter term of 2020 and summer term of 2021

Active Perception by Interaction with Other Agents in a Predictive Coding Framework: Application to Internet of Things Environment

Predicting the state of an agent\u27s partially-observable environment is a problem of interest in many domains. Typically in the real world, the environment consists of multiple agents, not necessarily working towards a common goal. Though the goal and sensory observation for each agent is unique, one agent might have acquired some knowledge that may benefit the other. In essence, the knowledge base regarding the environment is distributed among the agents. An agent can sample this distributed knowledge base by communicating with other agents. Since an agent is not storing the entire knowledge base, its model can be small and its inference can be efficient and fault-tolerant. However, the agent needs to learn -- when, with whom and what -- to communicate (in general interact) under different situations.This dissertation presents an agent model that actively and selectively communicates with other agents to predict the state of its environment efficiently. Communication is a challenge when the internal models of other agents is unknown and unobservable. The proposed agent learns communication policies as mappings from its belief state to when, with whom and what to communicate. The policies are learned using predictive coding in an online manner, without any reinforcement. The proposed agent model is evaluated on widely-studied applications, such as human activity recognition from multimodal, multisource and heterogeneous sensor data, and transferring knowledge across sensor networks. In the applications, either each sensor or each sensor network is assumed to be monitored by an agent. The recognition accuracy on benchmark datasets is comparable to the state-of-the-art, even though our model has significantly fewer parameters and infers the state in a localized manner. The learned policy reduces number of communications. The agent is tolerant to communication failures and can recognize the reliability of each agent from its communication messages. To the best of our knowledge, this is the first work on learning communication policies by an agent for predicting the state of its environment

Recolha, extração e classificação de opiniões sobre aplicações lúdicas para saúde e bem-estar

Nowadays, mobile apps are part of the life of anyone who owns a smartphone. With technological evolution, new apps come with new features, which brings a greater demand from users when using an application. Moreover, at a time when health and well-being are a priority, more and more apps provide a better user experience, not only in terms of health monitoring but also a pleasant experience in terms of entertainment and well-being. However, there are still some limitations regarding user experience and usability. What can best translate user satisfaction and experience are application reviews. Therefore, to have a perception of the most relevant aspects of the current applications, a collection of reviews and respective classifications was performed. This thesis aims to develop a system that allows the presentation of the most relevant aspects of a given health and wellness application after collecting the reviews and later extracting the aspects and classifying them. In the reviews collection task, two Python libraries, one for the Google Play Store and one for the App Store, provide methods for extracting data about an application. For the extraction and classification of aspects, the LCF-ATEPC model was chosen given its performance in aspects-based sentiment analysis studies.Atualmente, as aplicações móveis fazem parte da vida de qualquer pessoa que possua um smartphone. Com a evolução tecnológica, novas aplicações surgem com novas funcionalidades, o que traz uma maior exigência por parte dos utilizadores quando usam uma aplicação. Numa altura em que a saúde e bem-estar são uma prioridade, existem cada vez mais aplicações com o intuito de providenciar uma melhor experiência ao utilizador, não só a nível de monitorização de saúde, mas também de uma experiência agradável em termos de entertenimento e bem estar. Contudo, existem ainda algumas limitações no que toca à experiência e usabilidade do utilizador. O que melhor pode traduzir a satisfação e experiência do utilizador são as reviews das aplicações. Assim sendo, para ter uma perceção dos aspetos mais relevantes das atuais aplicações, foi feita uma recolha das reviews e respetivas classificações. O objetivo desta tese consiste no desenvolvimento de um sistema que permita apresentar os aspetos mais relevantes de uma determinada aplicação de saúde e bem estar, após a recolha das reviews e posterior extração dos aspetos e classificação dos mesmos. No processo de recolha de reviews, foram usadas duas bibliotecas em Python, uma relativa à Google Play Store e outra à App Store, que providenciam métodos para extrair dados relativamente a uma aplicação. Para a extração e classificação dos aspetos, o modelo LCF-ATEPC foi o escolhido dada a sua performance em estudos de análise de sentimento baseada em aspectos.Mestrado em Engenharia de Computadores e Telemátic

Transfer Learning in Human Activity Recognition: A Survey

Sensor-based human activity recognition (HAR) has been an active research area, owing to its applications in smart environments, assisted living, fitness, healthcare, etc. Recently, deep learning based end-to-end training has resulted in state-of-the-art performance in domains such as computer vision and natural language, where large amounts of annotated data are available. However, large quantities of annotated data are not available for sensor-based HAR. Moreover, the real-world settings on which the HAR is performed differ in terms of sensor modalities, classification tasks, and target users. To address this problem, transfer learning has been employed extensively. In this survey, we focus on these transfer learning methods in the application domains of smart home and wearables-based HAR. In particular, we provide a problem-solution perspective by categorizing and presenting the works in terms of their contributions and the challenges they address. We also present an updated view of the state-of-the-art for both application domains. Based on our analysis of 205 papers, we highlight the gaps in the literature and provide a roadmap for addressing them. This survey provides a reference to the HAR community, by summarizing the existing works and providing a promising research agenda.Comment: 40 pages, 5 figures, 7 table