Holographic deep learning for rapid optical screening of anthrax spores

Jo, YoungJu; Park, Sangjin; Jung, Jin‐Woo; Yoon, Jonghee; Joo, Hosung; Kim, Min-Hyeok; Kang, Suk‐Jo; Choi, Myung Chul; Lee, Sang Yup; Park, YongKeun

doi:10.1126/sciadv.1700606

Cited by 146 publications

(89 citation statements)

References 49 publications

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“…In other words, considering the proposed H‐SVM as a binary classifier, we are able to identify exactly MPs in pretreated seawater, thus discarding the other objects falling within the same range of characteristic scales. Previous works have proposed the use of holographic reconstructions to classify particles, cells, or microorganisms based on statistical methods or ML architectures . However, none of the existing ML‐DH approaches have tackled the problem of identifying MPs, which have their own specificity as the MP class consists of a wide heterogeneity of materials, morphologies, and characteristic scales.…”

Section: Discussionmentioning

confidence: 99%

Microplastic Identification via Holographic Imaging and Machine Learning

Bianco

Memmolo

Carcagnì

et al. 2020

Advanced Intelligent Systems

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Microplastics (MPs) are a major environmental concern due to their possible impact on water pollution, wildlife, and the food chain. Reliable, rapid, and high‐throughput screening of MPs from other components of a water sample after sieving and/or digestion is still a highly desirable goal to avoid cumbersome visual analysis by expert users under the optical microscope. Here, a new approach is presented that combines 3D coherent imaging with machine learning (ML) to achieve accurate and automatic detection of MPs in filtered water samples in a wide range at microscale. The water pretreatment process eliminates sediments and aggregates that fall out of the analyzed range. However, it is still necessary to clearly distinguish MPs from marine microalgae. Here, it is shown that, by defining a novel set of distinctive “holographic features,” it is possible to accurately identify MPs within the defined analysis range. The process is specifically tailored for characterizing the MPs' “holographic signatures,” thus boosting the classification performance and reaching accuracy higher than 99% in classifying thousands of items. The ML approach in conjunction with holographic coherent imaging is able to identify MPs independently from their morphology, size, and different types of plastic materials.

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Section: Discussionmentioning

confidence: 99%

Microplastic Identification via Holographic Imaging and Machine Learning

Bianco

Memmolo

Carcagnì

et al. 2020

Advanced Intelligent Systems

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“…Some improved label-free cell Raman scattering imaging techniques are used to study the cell chemical components, such as stimulated Raman scattering, surface enhanced Raman scattering and coherent antistokes Raman scattering, deep learning techniques have emerged in these studies as highperformance and powerful analytical tools (111,112). Deep learning is also applied to other advanced label-free cell imaging technologies, such as holographic microscopy (113), time stretch quantitative phase imaging (114), phase contrast microscopy (115), and hyperspectral imaging (116).…”

Section: Deep Learning In Single-cell Optical Imaging Techniquesmentioning

confidence: 99%

Deep Learning‐Based Single‐Cell Optical Image Studies

Sun

Tárnok

2020

Cytometry Pt A

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Optical imaging technology that has the advantages of high sensitivity and cost‐effectiveness greatly promotes the progress of nondestructive single‐cell studies. Complex cellular image analysis tasks such as three‐dimensional reconstruction call for machine‐learning technology in cell optical image research. With the rapid developments of high‐throughput imaging flow cytometry, big data cell optical images are always obtained that may require machine learning for data analysis. In recent years, deep learning has been prevalent in the field of machine learning for large‐scale image processing and analysis, which brings a new dawn for single‐cell optical image studies with an explosive growth of data availability. Popular deep learning techniques offer new ideas for multimodal and multitask single‐cell optical image research. This article provides an overview of the basic knowledge of deep learning and its applications in single‐cell optical image studies. We explore the feasibility of applying deep learning techniques to single‐cell optical image analysis, where popular techniques such as transfer learning, multimodal learning, multitask learning, and end‐to‐end learning have been reviewed. Image preprocessing and deep learning model training methods are then summarized. Applications based on deep learning techniques in the field of single‐cell optical image studies are reviewed, which include image segmentation, super‐resolution image reconstruction, cell tracking, cell counting, cross‐modal image reconstruction, and design and control of cell imaging systems. In addition, deep learning in popular single‐cell optical imaging techniques such as label‐free cell optical imaging, high‐content screening, and high‐throughput optical imaging cytometry are also mentioned. Finally, the perspectives of deep learning technology for single‐cell optical image analysis are discussed. © 2020 International Society for Advancement of Cytometry

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“…However, exogenous labeling agents were used in the diagnosis. Furthermore, quantitative phase‐contrast imaging (QPI) techniques combined with machine learning algorithms have been utilized to recognize types of cells or classify the states of biosamples, including bacteria , cancer cells , sperm cells , lymphocytes , macrophage activation , microorganisms , microobjects and RBCs . Since QPI techniques provide valuable phase information related with 3D morphology and biophysical properties of samples, iRBCs could be distinguished from healthy RBCs (hRBCs) with a relatively high accuracy (>91%) .…”

Section: Introductionmentioning

confidence: 99%

Machine learning‐based in‐line holographic sensing of unstained malaria‐infected red blood cells

Kim

Byeon

et al. 2018

Journal of Biophotonics

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Accurate and immediate diagnosis of malaria is important for medication of the infectious disease. Conventional methods for diagnosing malaria are time consuming and rely on the skill of experts. Therefore, an automatic and simple diagnostic modality is essential for healthcare in developing countries that lack the expertise of trained microscopists. In the present study, a new automatic sensing method using digital in-line holographic microscopy (DIHM) combined with machine learning algorithms was proposed to sensitively detect unstained malaria-infected red blood cells (iRBCs). To identify the RBC characteristics, 13 descriptors were extracted from segmented holograms of individual RBCs. Among the 13 descriptors, 10 features were highly statistically different between healthy RBCs (hRBCs) and iRBCs. Six machine learning algorithms were applied to effectively combine the dominant features and to greatly improve the diagnostic capacity of the present method. Among the classification models trained by the 6 tested algorithms, the model trained by the support vector machine (SVM) showed the best accuracy in separating hRBCs and iRBCs for training (n = 280, 96.78%) and testing sets (n = 120, 97.50%). This DIHM-based artificial intelligence methodology is simple and does not require blood staining. Thus, it will be beneficial and valuable in the diagnosis of malaria.

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Holographic deep learning for rapid optical screening of anthrax spores

Abstract: A synergistic application of holography and deep learning enables rapid optical screening of anthrax spores and other pathogens.

Cited by 146 publications

References 49 publications

Microplastic Identification via Holographic Imaging and Machine Learning

Microplastic Identification via Holographic Imaging and Machine Learning

Deep Learning‐Based Single‐Cell Optical Image Studies

Machine learning‐based in‐line holographic sensing of unstained malaria‐infected red blood cells

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