Dr. Phuong Nguyen Profile

Dr. Phuong Nguyen

at Univ of Maryland Baltimore County

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Publications (4)

Proceedings Article | 4 April 2022 Presentation

Pairwise meta learning pipeline: classifying COVID-19 abnormalities on chest radio-graphs

Sourajit Saha, Yaacov Yesha, Yelena Yesha, Aryya Gangopadhyay, David Chapman, Michael Morris, Babak Saboury, Phuong Nguyen

Proceedings Volume PC12033, PC1203302 (2022) https://doi.org/10.1117/12.2613235

KEYWORDS: Chest, Computer aided diagnosis and therapy, Solid modeling, Medicine, Imaging informatics, Image classification, Convolutional neural networks, Computer aided design, Classification systems, CAD systems

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Proceedings Article | 22 April 2020 Paper

Satellite data fusion of multiple observed XCO2 using compressive sensing

Phuong Nguyen, Samit Shivadekar, Sai Sree Laya Chukkapalli, Milton Halem

Proceedings Volume 11423, 114230Y (2020) https://doi.org/10.1117/12.2558319

KEYWORDS: Satellites, Data fusion, Compressed sensing, Carbon dioxide, Sensors, Image fusion, Spatial resolution, Signal processing, Earth observing sensors, Carbon

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When it entered into the era of big data, Earth observing systems developed into a new stage, namely characterized by low cost, multi-national, multi-sensor and multi-modal with varying spatial and spectral resolutions confronting new challenges and opportunities. Climate data records from multiple data sources are used to infer seasonal and interannual variations which will advance and promote the development of data fusion methods. Compressed sensing is a new framework in which data acquisition and data processing are merged. It provides a new fantastic way to handle multiple observations of the same field view from complementary remote sensing instruments, allowing us to recover information at very low signal-to-noise ratio. We will particularly point out that a Compressive Sensing based framework is flexible enough for combining the two measurement systems by fusing the data from the two satellites, NASA Orbiting Carbon Observatory -2 (OCO-2) and the JAXA Greenhouse gases from Orbiting Satellites (GOSAT) to calculate the interannual Net XCO2 variability over land for three latitudinal regions, Alaska/Canada, United States and the Amazon/Brazil. The OCO-2 design is optimized for sensitivity to XCO2 variations, with an unprecedented combination of spatial resolution (about 3km) with narrow nadir coverage, while GOSAT provides broader spatial coverage (10km) with wider scanning coverage. There are different temporal degradations of both instruments over time because GOSAT was launched in 2009 and OCO-2 was launched in 2014. Both instruments infer CO2 concentration from high-resolution measurements of reflected sunlight and use similar inversion algorithms to retrieve CO2 concentrations. Both are passive satellites providing on-orbit global measurements of the greenhouse gas, XCO2, for the years 2015 -2018. The results of the CS data fusion framework show that the fused data have Root Mean Square Error (RMSE) varying from 1.31 ppm to 4.12 ppm compared with original data, depending on the region of study and gridding resolution. Validation of fused data compared with AmeriFlux station towers observations shows RMSE of 2.68 ppm.

Proceedings Article | 16 March 2020 Paper

Active semi-supervised expectation maximization learning for lung cancer detection from Computerized Tomography (CT) images with minimally label training data

Phuong Nguyen, David Chapman, Sumeet Menon, Michael Morris, Yelena Yesha

Proceedings Volume 11314, 113142E (2020) https://doi.org/10.1117/12.2549655

KEYWORDS: Expectation maximization algorithms, Lung cancer, Computed tomography, Cancer, Computer aided diagnosis and therapy, Radiology, Artificial intelligence

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Artificial intelligence (AI) has great potential in medical imaging to augment the clinician as a virtual radiology assistant (vRA) through enriching information and providing clinical decision support. Deep learning is a type of AI that has shown promise in performance for Computer Aided Diagnosis (CAD) tasks. A current barrier to implementing deep learning for clinical CAD tasks in radiology is that it requires a training set to be representative and as large as possible in order to generalize appropriately and achieve high accuracy predictions. There is a lack of available, reliable, discretized and annotated labels for computer vision research in radiology despite the abundance of diagnostic imaging examinations performed in routine clinical practice. Furthermore, the process to create reliable labels is tedious, time consuming and requires expertise in clinical radiology. We present an Active Semi-supervised Expectation Maximization (ASEM) learning model for training a Convolutional Neural Network (CNN) for lung cancer screening using Computed Tomography (CT) imaging examinations. Our learning model is novel since it combines Semi-supervised learning via the Expectation-Maximization (EM) algorithm with Active learning via Bayesian experimental design for use with 3D CNNs for lung cancer screening. ASEM simultaneously infers image labels as a latent variable, while predicting which images, if additionally labeled, are likely to improve classification accuracy. The performance of this model has been evaluated using three publicly available chest CT datasets: Kaggle2017, NLST, and LIDC-IDRI. Our experiments showed that ASEM-CAD can identify suspicious lung nodules and detect lung cancer cases with an accuracy of 92% (Kaggle17), 93% (NLST), and 73% (LIDC) and Area Under Curve (AUC) of 0.94 (Kaggle), 0.88 (NLST), and 0.81 (LIDC). These performance numbers are comparable to fully supervised training, but use only slightly more than 50% of the training data labels .

Proceedings Article | 18 May 2013 Paper

A decade of measured greenhouse forcings from AIRS

D. Chapman, P. Nguyen, M. Halem

Proceedings Volume 8743, 874313 (2013) https://doi.org/10.1117/12.2017019

KEYWORDS: Absorption, Infrared radiation, Calibration, MODIS, Climate change, Carbon dioxide, Climatology, Satellites, Temperature metrology, Spectral resolution

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