Advancements in Medical Imaging and Diagnostics with Deep Learning Technologies

Medical imaging has long been a cornerstone in diagnostic medicine, providing clinicians with a non-invasive method to visualize internal structures and processes. However, traditional imaging techniques have faced challenges in resolution, safety concerns related to radiation exposure, and the need for invasive procedures for clearer visualization. With the advent of deep learning technologies, significant advancements have been made in the field of medical imaging, addressing many of these challenges and introducing new capabilities. This research seeks into the integration of deep learning in enhancing image resolution, leading to clearer and more detailed visualizations. Furthermore, the ability to reconstruct three-dimensional images from traditional two-dimensional scans offers a more comprehensive view of the area under examination. Automated analysis powered by deep learning algorithms not only speeds up the diagnostic process but also detects anomalies that might be overlooked by the human eye. Predictive analysis, based on these enhanced images, can forecast the likelihood of diseases, and real-time analysis during surgeries ensures immediate feedback, enhancing the precision of medical procedures. Safety in medical imaging has also seen improvements. Techniques powered by deep learning require reduced radiation, minimizing risks to patients. Additionally, the enhanced clarity and detail in images reduce the need for invasive procedures, further ensuring patient safety. The integration of imaging data with Electronic Health Records (EHR) has paved the way for personalized care recommendations, tailoring treatments based on individual patient history and current diagnostics. Lastly, the role of deep learning extends to medical education, where it aids in creating realistic simulations and models, equipping medical professionals with better training tools

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

Outlier detection techniques for wireless sensor networks: A survey

In the field of wireless sensor networks, those measurements that significantly deviate from the normal pattern of sensed data are considered as outliers. The potential sources of outliers include noise and errors, events, and malicious attacks on the network. Traditional outlier detection techniques are not directly applicable to wireless sensor networks due to the nature of sensor data and specific requirements and limitations of the wireless sensor networks. This survey provides a comprehensive overview of existing outlier detection techniques specifically developed for the wireless sensor networks. Additionally, it presents a technique-based taxonomy and a comparative table to be used as a guideline to select a technique suitable for the application at hand based on characteristics such as data type, outlier type, outlier identity, and outlier degree

An objective based classification of aggregation techniques for wireless sensor networks

Wireless Sensor Networks have gained immense popularity in recent years due to their ever increasing capabilities and wide range of critical applications. A huge body of research efforts has been dedicated to find ways to utilize limited resources of these sensor nodes in an efficient manner. One of the common ways to minimize energy consumption has been aggregation of input data. We note that every aggregation technique has an improvement objective to achieve with respect to the output it produces. Each technique is designed to achieve some target e.g. reduce data size, minimize transmission energy, enhance accuracy etc. This paper presents a comprehensive survey of aggregation techniques that can be used in distributed manner to improve lifetime and energy conservation of wireless sensor networks. Main contribution of this work is proposal of a novel classification of such techniques based on the type of improvement they offer when applied to WSNs. Due to the existence of a myriad of definitions of aggregation, we first review the meaning of term aggregation that can be applied to WSN. The concept is then associated with the proposed classes. Each class of techniques is divided into a number of subclasses and a brief literature review of related work in WSN for each of these is also presented