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The philosophical foundations of modern medicine
An exploration of the philosophical foundation of modern medicine which explains why such a medicine possesses the characteristics it does and where precisely its strengths as well as its weaknesses lie. Written in plain English, it should be accessible to anyone who is intellectually curious, lay-persons, and medical professionals alike. --From publisher description.
Book
Fifty Years of Biomedical Engineering Undergraduate Education
Undergraduate education in biomedical engineering (BME) and bioengineering (BioE) has been in place for more than 50 years. It has been important in shaping the field as a whole. The early undergraduate programs developed shortly after BME graduate programs, as universities sought to capitalize on the interest of students and the practical advantages of having BME departments that could control their own resources and curriculum. Unlike other engineering fields, BME did not rely initially on a market for graduates in industry, although BME graduates subsequently have found many opportunities. BME undergraduate programs exploded in the 2000s with funding from the Whitaker Foundation and resources from other agencies such as the National Institute of Biomedical Imaging and Bioengineering. The number of programs appears to be reaching a plateau, with 118 accredited programs in the United States at present. We show that there is a core of material that most undergraduates are expected to know, which is different from the knowledge base of other engineers not only in terms of biology, but in the breadth of engineering. We also review the role of important organizations and conferences in the growth of BME, special features of BME education, first placements of BME graduates, and a few challenges to address in the future.
Journal Article
Why rankings of biomedical image analysis competitions should be interpreted with care
International challenges have become the standard for validation of biomedical image analysis methods. Given their scientific impact, it is surprising that a critical analysis of common practices related to the organization of challenges has not yet been performed. In this paper, we present a comprehensive analysis of biomedical image analysis challenges conducted up to now. We demonstrate the importance of challenges and show that the lack of quality control has critical consequences. First, reproducibility and interpretation of the results is often hampered as only a fraction of relevant information is typically provided. Second, the rank of an algorithm is generally not robust to a number of variables such as the test data used for validation, the ranking scheme applied and the observers that make the reference annotations. To overcome these problems, we recommend best practice guidelines and define open research questions to be addressed in the future.
Biomedical image analysis challenges have increased in the last ten years, but common practices have not been established yet. Here the authors analyze 150 recent challenges and demonstrate that outcome varies based on the metrics used and that limited information reporting hampers reproducibility.
Journal Article
A Review of Machine Learning Algorithms for Biomedical Applications
As the amount and complexity of biomedical data continue to increase, machine learning methods are becoming a popular tool in creating prediction models for the underlying biomedical processes. Although all machine learning methods aim to fit models to data, the methodologies used can vary greatly and may seem daunting at first. A comprehensive review of various machine learning algorithms per biomedical applications is presented. The key concepts of machine learning are supervised and unsupervised learning, feature selection, and evaluation metrics. Technical insights on the major machine learning methods such as decision trees, random forests, support vector machines, and k-nearest neighbors are analyzed. Next, the dimensionality reduction methods like principal component analysis and t-distributed stochastic neighbor embedding methods, and their applications in biomedical data analysis were reviewed. Moreover, in biomedical applications predominantly feedforward neural networks, convolutional neural networks, and recurrent neural networks are utilized. In addition, the identification of emerging directions in machine learning methodology will serve as a useful reference for individuals involved in biomedical research, clinical practice, and related professions who are interested in understanding and applying machine learning algorithms in their research or practice.
Journal Article
MXene in the lens of biomedical engineering: synthesis, applications and future outlook
MXene is a recently emerged multifaceted two-dimensional (2D) material that is made up of surface-modified carbide, providing its flexibility and variable composition. They consist of layers of early transition metals (M), interleaved with
n
layers of carbon or nitrogen (denoted as X) and terminated with surface functional groups (denoted as T
x
/T
z
) with a general formula of M
n+1
X
n
T
x
, where
n
= 1–3. In general, MXenes possess an exclusive combination of properties, which include, high electrical conductivity, good mechanical stability, and excellent optical properties. MXenes also exhibit good biological properties, with high surface area for drug loading/delivery, good hydrophilicity for biocompatibility, and other electronic-related properties for computed tomography (CT) scans and magnetic resonance imaging (MRI). Due to the attractive physicochemical and biocompatibility properties, the novel 2D materials have enticed an uprising research interest for application in biomedicine and biotechnology. Although some potential applications of MXenes in biomedicine have been explored recently, the types of MXene applied in the perspective of biomedical engineering and biomedicine are limited to a few, titanium carbide and tantalum carbide families of MXenes. This review paper aims to provide an overview of the structural organization of MXenes, different top-down and bottom-up approaches for synthesis of MXenes, whether they are fluorine-based or fluorine-free etching methods to produce biocompatible MXenes. MXenes can be further modified to enhance the biodegradability and reduce the cytotoxicity of the material for biosensing, cancer theranostics, drug delivery and bio-imaging applications. The antimicrobial activity of MXene and the mechanism of MXenes in damaging the cell membrane were also discussed. Some challenges for in vivo applications, pitfalls, and future outlooks for the deployment of MXene in biomedical devices were demystified. Overall, this review puts into perspective the current advancements and prospects of MXenes in realizing this 2D nanomaterial as a versatile biological tool.
Journal Article
Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl
Segmenting the nuclei of cells in microscopy images is often the first step in the quantitative analysis of imaging data for biological and biomedical applications. Many bioimage analysis tools can segment nuclei in images but need to be selected and configured for every experiment. The 2018 Data Science Bowl attracted 3,891 teams worldwide to make the first attempt to build a segmentation method that could be applied to any two-dimensional light microscopy image of stained nuclei across experiments, with no human interaction. Top participants in the challenge succeeded in this task, developing deep-learning-based models that identified cell nuclei across many image types and experimental conditions without the need to manually adjust segmentation parameters. This represents an important step toward configuration-free bioimage analysis software tools.The 2018 Data Science Bowl challenged competitors to develop an accurate tool for segmenting stained nuclei from diverse light microscopy images. The winners deployed innovative deep-learning strategies to realize configuration-free segmentation.
Journal Article