Benchmarking HEp-2 specimen cells classification using linear discriminant analysis on higher order spectra features of cell shape

Al-Dulaimi, Khamael; Chandran, Vinod; Nguyen, Kien; Banks, Jasmine; Tomeo-Reyes, Inmaculada

doi:10.1016/j.patrec.2019.06.020

Cited by 35 publications

(16 citation statements)

References 24 publications

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“…Recently, machine learning (ML) has become very widespread in research and has been incorporated in a variety of applications, including text mining, spam detection, video recommendation, image classification, and multimedia concept retrieval [1][2][3][4][5][6]. Among the different ML algorithms, deep learning (DL) is very commonly employed in these applications [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

et al. 2021

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In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, among many others. Despite it has been contributed several works reviewing the State-of-the-Art on DL, all of them only tackled one aspect of the DL, which leads to an overall lack of knowledge about it. Therefore, in this contribution, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of DL. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field. In particular, this paper outlines the importance of DL, presents the types of DL techniques and networks. It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet network and closing with the High-Resolution network (HR.Net). Finally, we further present the challenges and suggested solutions to help researchers understand the existing research gaps. It is followed by a list of the major DL applications. Computational tools including FPGA, GPU, and CPU are summarized along with a description of their influence on DL. The paper ends with the evolution matrix, benchmark datasets, and summary and conclusion.

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Section: Introductionmentioning

confidence: 99%

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

et al. 2021

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“…When used for dimensionality reduction, LDA relies on the class labels of each observation and projects the data onto an axis that maximizes the separation between the classes 40 . LDA is increasingly being used in biomedical applications to differentiate between healthy and diseased states or to classify various cell states within the same cell line [41][42][43] . We utilized SL concentrations as the input features and the IDO activity + IFN-γ treatment (low IDO unstimulated, low IDO IFN-γ primed, high IDO unstimulated, high IDO IFN-γ primed) as the class labels in LDA.…”

Section: Linear Discriminant Analysis Classification Of Mscsmentioning

confidence: 99%

Section: We Investigated the Relationship Between Ido Activity Levels And Thementioning

confidence: 99%

Characterizing Human Mesenchymal Stromal Cells Immune Modulatory Potency Using Targeted Lipidomic Profiling of Sphingolipids

DeVeaux

Vyshnya

et al. 2021

Preprint

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Cell therapies are expected to increase over the next decade due to increasing demand for clinical applications. Mesenchymal stromal cells (MSCs) have been explored to treat a number of diseases, with some successes in early clinical trials. Despite early successes, poor MSC characterization results in lessened therapeutic capacity once in vivo. Here, we characterized bone marrow (BM), adipose derived and umbilical cord tissue MSCs sphingolipids (SLs), a class of bioactive lipids, using liquid chromatography tandem mass spectrometry. We found ceramide levels differed based upon donors sex in BM MSCs. We detected fatty acyl chain variants in MSCs from all 3 sources. Principal component analysis showed IFNg; primed and untreated MSCs separated according to their SL signature. We detected higher ceramide levels in low IDO MSCs, indicating sphingomeylinase or ceramidase enzymatic activity may be involved in their immune potency. Lastly, linear discriminant analysis revealed that MSCs separated based on tissue source.

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“…Several approaches [ 11–21 ] have been proposed for automated image analysis of SEM and TEM images. However, most of these approaches rely on single thresholds for the feature separation, [ 20,21 ] encounter major difficulties caused by irregular object patterns and noise, [ 22 ] or they rely on hand‐crafted features for the particle shapes, [ 14,15 ] which impair the generalization potential of such algorithms for the characterization of arbitrary nanoparticles or heterogeneous nanoparticle ensembles.…”

Section: Introductionmentioning

confidence: 99%

Synthetic Image Rendering Solves Annotation Problem in Deep Learning Nanoparticle Segmentation

Mill

Wolff²,

Gerrits

et al. 2021

Small Methods

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and are important in various technological fields such as energy, electronics, medicine, and many more. [1][2][3][4][5] However, as a consequence of industrial processes and man-made pollution, unwanted nanoparticle size distributions and concentrations [6] give rise to concerns with respect to human health and environmental pollution. While the nanoparticles' physicochemical properties (size, shape, surface chemistry, etc.) determine the quality of products, [7,8] such characteristics are also important in order to evaluate the biological impact of nanoparticles at a molecular, cellular, and systemic level for any risk assessment for environmental and human health. [9] Characterizing nanoparticles in a dynamic context and on a case-by-case basis, microscopic imaging techniques including those that use focused electron or ion beams in scanning electron microscopes (SEMs) or helium ion microscopes [10] (HIMs) to generate nanometer scale spatial resolution are frequently applied in the scientific community. Given the substantial information content of digital images, these techniques often benefit from, or require, automated high-throughput data analysis that enables the accurate identification of large numbers of particles in a robust way.Nanoparticles occur in various environments as a consequence of man-made processes, which raises concerns about their impact on the environment and human health. To allow for proper risk assessment, a precise and statistically relevant analysis of particle characteristics (such as size, shape, and composition) is required that would greatly benefit from automated image analysis procedures. While deep learning shows impressive results in object detection tasks, its applicability is limited by the amount of representative, experimentally collected and manually annotated training data. Here, an elegant, flexible, and versatile method to bypass this costly and tedious data acquisition process is presented. It shows that using a rendering software allows to generate realistic, synthetic training data to train a state-of-the art deep neural network. Using this approach, a segmentation accuracy can be derived that is comparable to man-made annotations for toxicologically relevant metal-oxide nanoparticle ensembles which were chosen as examples. The presented study paves the way toward the use of deep learning for automated, highthroughput particle detection in a variety of imaging techniques such as in microscopies and spectroscopies, for a wide range of applications, including the detection of micro-and nanoplastic particles in water and tissue samples.

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Benchmarking HEp-2 specimen cells classification using linear discriminant analysis on higher order spectra features of cell shape

Cited by 35 publications

References 24 publications

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Characterizing Human Mesenchymal Stromal Cells Immune Modulatory Potency Using Targeted Lipidomic Profiling of Sphingolipids

Synthetic Image Rendering Solves Annotation Problem in Deep Learning Nanoparticle Segmentation

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