Dynamic imaging and characterization of volatile aerosols using deep learning-based holographic microscopy

2021 ◽  
Author(s):  
Yi Luo ◽  
Yichen Wu ◽  
Liqiao Li ◽  
Yuening Guo ◽  
Ege Çetintaş ◽  
...  
Keyword(s):  
Deep Learning ◽  
Dynamic Imaging ◽  
Holographic Microscopy

scholarly journals Dynamic Imaging and Characterization of Volatile Aerosols in E-Cigarette Emissions Using Deep Learning-Based Holographic Microscopy

ACS Sensors ◽  
2021 ◽  
Author(s):  
Yi Luo ◽  
Yichen Wu ◽  
Liqiao Li ◽  
Yuening Guo ◽  
Ege Çetintaş ◽  
...  
Keyword(s):  
Deep Learning ◽  
Dynamic Imaging ◽  
Holographic Microscopy

scholarly journals Characterization of Optical Coherence Tomography Images for Colon Lesion Differentiation under Deep Learning

2021 ◽  
Vol 11 (7) ◽  
pp. 3119
Author(s):  
Cristina L. Saratxaga ◽  
Jorge Bote ◽  
Juan F. Ortega-Morán ◽  
Artzai Picón ◽  
Elena Terradillos ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Data Augmentation ◽  
Optical Biopsy ◽  
Colon Polyps ◽  
Optical Coherence ◽  
Colon Lesion ◽  
Patient Prognosis ◽  
B Scan Images

(1) Background: Clinicians demand new tools for early diagnosis and improved detection of colon lesions that are vital for patient prognosis. Optical coherence tomography (OCT) allows microscopical inspection of tissue and might serve as an optical biopsy method that could lead to in-situ diagnosis and treatment decisions; (2) Methods: A database of murine (rat) healthy, hyperplastic and neoplastic colonic samples with more than 94,000 images was acquired. A methodology that includes a data augmentation processing strategy and a deep learning model for automatic classification (benign vs. malignant) of OCT images is presented and validated over this dataset. Comparative evaluation is performed both over individual B-scan images and C-scan volumes; (3) Results: A model was trained and evaluated with the proposed methodology using six different data splits to present statistically significant results. Considering this, 0.9695 (±0.0141) sensitivity and 0.8094 (±0.1524) specificity were obtained when diagnosis was performed over B-scan images. On the other hand, 0.9821 (±0.0197) sensitivity and 0.7865 (±0.205) specificity were achieved when diagnosis was made considering all the images in the whole C-scan volume; (4) Conclusions: The proposed methodology based on deep learning showed great potential for the automatic characterization of colon polyps and future development of the optical biopsy paradigm.

Deep learning based joint segmentation and characterization of multi-class retinal fluid lesions on OCT scans for clinical use in anti-VEGF therapy

2021 ◽  
pp. 104727
Author(s):  
Bilal Hassan ◽  
Shiyin Qin ◽  
Ramsha Ahmed ◽  
Taimur Hassan ◽  
Abdel Hakeem Taguri ◽  
...  
Keyword(s):  
Deep Learning ◽  
Clinical Use ◽  
Anti Vegf

scholarly journals Deep Learning Approach for Analysis and Characterization of COVID-19

2022 ◽  
Vol 70 (1) ◽  
pp. 451-468
Author(s):  
Indrajeet Kumar ◽  
Sultan S. Alshamrani ◽  
Abhishek Kumar ◽  
Jyoti Rawat ◽  
Kamred Udham Singh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Approach

scholarly journals Text Data Augmentation for Deep Learning

2021 ◽  
Author(s):  
Connor Shorten ◽  
Taghi M. Khoshgoftaar ◽  
Borko Furht
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Data Augmentation ◽  
Early Stage ◽  
Practical Implementation ◽  
Text Data ◽  
Training Strategy ◽  
Local Decision ◽  
Decision Boundaries

Abstract Natural Language Processing (NLP) is one of the most captivating applications of Deep Learning. In this survey, we consider how the Data Augmentation training strategy can aid in its development. We begin with the major motifs of Data Augmentation summarized into strengthening local decision boundaries, brute force training, causality and counterfactual examples, and the distinction between meaning and form. We follow these motifs with a concrete list of augmentation frameworks that have been developed for text data. Deep Learning generally struggles with the measurement of generalization and characterization of overfitting. We highlight studies that cover how augmentations can construct test sets for generalization. NLP is at an early stage in applying Data Augmentation compared to Computer Vision. We highlight the key differences and promising ideas that have yet to be tested in NLP. For the sake of practical implementation, we describe tools that facilitate Data Augmentation such as the use of consistency regularization, controllers, and offline and online augmentation pipelines, to preview a few. Finally, we discuss interesting topics around Data Augmentation in NLP such as task-specific augmentations, the use of prior knowledge in self-supervised learning versus Data Augmentation, intersections with transfer and multi-task learning, and ideas for AI-GAs (AI-Generating Algorithms). We hope this paper inspires further research interest in Text Data Augmentation.

scholarly journals Automatic phase aberration compensation for digital holographic microscopy based on deep learning background detection

2017 ◽  
Vol 25 (13) ◽  
pp. 15043 ◽  
Author(s):  
Thanh Nguyen ◽  
Vy Bui ◽  
Van Lam ◽  
Christopher B. Raub ◽  
Lin-Ching Chang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Digital Holographic Microscopy ◽  
Phase Aberration ◽  
Automatic Phase ◽  
Holographic Microscopy

Application of Deep Learning for Characterization of CO2 Leakage Based on Above Zone Monitoring Interval (AZMI) Pressure Data

2021 ◽  
Author(s):  
Kristian Gundersen ◽  
Seyyed Hosseini ◽  
Anna Oleynik ◽  
Guttorm Alendal
Keyword(s):  
Deep Learning ◽  
Co2 Leakage ◽  
Pressure Data
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