Dr. Hong-Jun Yoon Profile

Dr. Hong-Jun Yoon

Research Scientist at Oak Ridge National Laboratory

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Publications (17)

Proceedings Article | 3 April 2024 Poster

Enhancing diagnosis through AI-driven analysis of reflectance confocal microscopy

Hong-Jun Yoon, Chris Keum, Alexander Witkowski, Joanna Ludzik, Tracy Petrie, Heidi Hanson, Sancy Leachman

Proceedings Volume 12927, 1292728 (2024) https://doi.org/10.1117/12.3006793

KEYWORDS: Image segmentation, Evolutionary algorithms, Feature extraction, Education and training, Tissues, Machine learning, Skin, Reflectivity, Confocal microscopy, Biopsy

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Proceedings Article | 7 April 2023 Poster + Paper

A comparison of histopathology imaging comprehension algorithms based on multiple instance learning

Adam Saunders, Sajal Dash, Aristeidis Tsaris, Hong-Jun Yoon

Proceedings Volume 12471, 124711M (2023) https://doi.org/10.1117/12.2654265

KEYWORDS: Education and training, Transformers, Data modeling, Machine learning, Tumor growth modeling, Tissues, Deep learning, Cancer, Biological samples, Evolutionary algorithms

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Proceedings Article | 4 April 2022 Poster + Presentation + Paper

Image transformers for classifying acute lymphoblastic leukemia

Priscilla Cho, Sajal Dash, Aristeides Tsaris, Hong-Jun Yoon

Proceedings Volume 12033, 120332F (2022) https://doi.org/10.1117/12.2611496

KEYWORDS: Transformers, Blood, Leukemia, Cancer, Visual process modeling, Bone, Statistical modeling, Data modeling, Tumor growth modeling, Machine learning

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Cancer is the leading cause of death by disease in American children. Each year, nearly 16,000 children in the United States and over 300,000 children globally are diagnosed with cancer. Leukemia is a form of blood cancer that originates in the bone marrow and accounts for one-third of pediatric cancers. This disease occurs when the bone marrow contains 20% or more immature white blood cell blasts. Acute lymphoblastic leukemia is the most prevalent leukemia type found in children, with half of all annual cases in the U.S. diagnosed for subjects under 20 years of age. To diagnose acute lymphoblastic leukemia, pathologists often conduct a morphological bone marrow assessment. This assessment determines whether the immature white blood cell blasts in bone marrow display the correct morphological characteristics, such as size and appearance of nuclei. Pathologists also use immunophenotyping via multi-channel flow cytometry to test whether certain antigens are present on the surface of blast cells; the antigens are used to identify the cell lineage of acute lymphoblastic leukemia. These manual processes require well-trained personnel and medical professionals, thus being costly in time and expenses. Computerized decision support via machine learning can accelerate the diagnosis process and reduce the cost. Training a reliable classification model to distinguish between mature and immature white blood cells is essential to the decision support system. Here, we adopted the Vision Transformer model to classify white blood cells. The Vision Transformer achieved superb classification performance compared to state-of-the-art convolutional neural networks while requiring less computational resources for training. Additionally, the latent self-attention architecture provided attention maps for a given image, providing clues as to which portion(s) of the image were significant in decision-making. We applied the Vision Transformer model and a convolutional neural network model to an acute lymphoblastic leukemia classification dataset of 12,528 samples and achieved accuracies of 88.4% and 86.2%.

Proceedings Article | 15 February 2021 Poster + Presentation + Paper

Evaluation of U-net-based image segmentation model to digital mammography

Priscilla Cho, Hong-Jun Yoon

Proceedings Volume 11596, 1159626 (2021) https://doi.org/10.1117/12.2581401

KEYWORDS: Image segmentation, Systems modeling, Solid modeling, Digital mammography, Tissues, Performance modeling, Medical imaging, Breast, Algorithm development, Tumor growth modeling

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Proceedings Article | 16 March 2020 Paper

Survey of image denoising methods for medical image classification

Peter Michael, Hong-Jun Yoon

Proceedings Volume 11314, 113143R (2020) https://doi.org/10.1117/12.2549695

KEYWORDS: Denoising, Image classification, Medical imaging, Digital filtering, Electronic filtering, Performance modeling, Image denoising, Data modeling, Image filtering, Filtering (signal processing)

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Proceedings Article | 13 March 2019 Paper

Computer-aided detection using non-convolutional neural network Gaussian processes

Devanshu Agrawal, Hong-Jun Yoon, Georgia Tourassi, Jacob Hinkle

Proceedings Volume 10950, 109503N (2019) https://doi.org/10.1117/12.2513625

KEYWORDS: Data modeling, Performance modeling, Neural networks, Solid modeling, Computer-aided diagnosis, Computer aided diagnosis and therapy, Binary data, Image classification, Computer aided design, Process modeling

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Proceedings Article | 6 July 2018 Paper

Deep radiogenomics for predicting clinical phenotypes in invasive breast cancer

Hong-Jun Yoon, Arvind Ramanathan, Folami Alamudun, Georgia Tourassi

Proceedings Volume 10718, 107181H (2018) https://doi.org/10.1117/12.2318508

KEYWORDS: Breast cancer, Genomics, Receptors, Magnetic resonance imaging, Cancer, Tumors, Data archive systems, Breast, Oncology, Convolutional neural networks

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SPIE Journal Paper | 19 March 2018 Open Access

Modeling sequential context effects in diagnostic interpretation of screening mammograms

Folami Alamudun, Paige Paulus, Hong-Jun Yoon, Georgia Tourassi

JMI, Vol. 5, Issue 03, 031408, (March 2018) https://doi.org/10.1117/12.10.1117/1.JMI.5.3.031408

KEYWORDS: Diagnostics, Visualization, Mammography, Breast, Radiology, Data modeling, Statistical analysis, Visual analytics, Medical imaging, Fractal analysis

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Prior research has shown that physicians’ medical decisions can be influenced by sequential context, particularly in cases where successive stimuli exhibit similar characteristics when analyzing medical images. This type of systematic error is known to psychophysicists as sequential context effect as it indicates that judgments are influenced by features of and decisions about the preceding case in the sequence of examined cases, rather than being based solely on the peculiarities unique to the present case. We determine if radiologists experience some form of context bias, using screening mammography as the use case. To this end, we explore correlations between previous perceptual behavior and diagnostic decisions and current decisions. We hypothesize that a radiologist’s visual search pattern and diagnostic decisions in previous cases are predictive of the radiologist’s current diagnostic decisions. To test our hypothesis, we tasked 10 radiologists of varied experience to conduct blind reviews of 100 four-view screening mammograms. Eye-tracking data and diagnostic decisions were collected from each radiologist under conditions mimicking clinical practice. Perceptual behavior was quantified using the fractal dimension of gaze scanpath, which was computed using the Minkowski–Bouligand box-counting method. To test the effect of previous behavior and decisions, we conducted a multifactor fixed-effects ANOVA. Further, to examine the predictive value of previous perceptual behavior and decisions, we trained and evaluated a predictive model for radiologists’ current diagnostic decisions. ANOVA tests showed that previous visual behavior, characterized by fractal analysis, previous diagnostic decisions, and image characteristics of previous cases are significant predictors of current diagnostic decisions. Additionally, predictive modeling of diagnostic decisions showed an overall improvement in prediction error when the model is trained on additional information about previous perceptual behavior and diagnostic decisions.

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Proceedings Article | 24 March 2016 Paper

Shapelet analysis of pupil dilation for modeling visuo-cognitive behavior in screening mammography

Folami Alamudun, Hong-Jun Yoon, Tracy Hammond, Kathy Hudson, Garnetta Morin-Ducote, Georgia Tourassi

Proceedings Volume 9787, 97870M (2016) https://doi.org/10.1117/12.2217670

KEYWORDS: Mammography, Diagnostics, Eye models, Pathology, Breast, Visual process modeling, Visual analytics, Visualization, Eye, Optical tracking, Radiology, Data modeling, Cancer, Breast cancer, Nervous system

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Proceedings Article | 17 March 2015 Paper

Fractal analysis of radiologists' visual scanning pattern in screening mammography

Folami Alamudun, Hong-Jun Yoon, Kathy Hudson, Garnetta Morin-Ducote, Georgia Tourassi

Proceedings Volume 9416, 94160T (2015) https://doi.org/10.1117/12.2082972

KEYWORDS: Visualization, Pathology, Fractal analysis, Mammography, Diagnostics, Visual analytics, Breast, Radiology, Image processing, Visual process modeling

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Proceedings Article | 17 March 2015 Paper

Temporal stability of visual search-driven biometrics

Hong-Jun Yoon, Tandy Carmichael, Georgia Tourassi

Proceedings Volume 9416, 94160U (2015) https://doi.org/10.1117/12.2082801

KEYWORDS: Visualization, Biometrics, Calibration, Glasses, Phase modulation, Eye, Data modeling, Visual process modeling, Cognitive modeling, Eye models

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Proceedings Article | 11 March 2014 Paper

Gaze as a biometric

Hong-Jun Yoon, Tandy Carmichael, Georgia Tourassi

Proceedings Volume 9037, 903707 (2014) https://doi.org/10.1117/12.2044303

KEYWORDS: Biometrics, Eye, Data modeling, Eye models, Visualization, Calibration, Data conversion, Radiology, Glasses, Profiling

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Proceedings Article | 22 February 2012 Paper

Inverse dependence of search and classification performances in lesion localization tasks

D. Chakraborty, Hong-Jun Yoon, Claudia Mello-Thoms

Proceedings Volume 8318, 83180H (2012) https://doi.org/10.1117/12.911820

KEYWORDS: Binary data, Computer aided diagnosis and therapy, Image classification, Data modeling, Electronic filtering, Mammography, Performance modeling, Chest, Statistical analysis, Computer simulations

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Proceedings Article | 13 March 2009 Paper

JAFROC analysis revisited: figure-of-merit considerations for human observer studies

D. Chakraborty, Hong-Jun Yoon

Proceedings Volume 7263, 72630T (2009) https://doi.org/10.1117/12.810859

KEYWORDS: Statistical analysis, Receivers, Computer simulations, Electronic filtering, Data modeling, Analytical research, Statistical modeling, Interference (communication), Process modeling, Radiology

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Jackknife alternative free-response receiver operating characteristic (JAFROC) is a method for measuring human observer performance in localization tasks. JAFROC is being increasingly used to evaluate imaging modalities because it has been shown to have greater statistical power than conventional receiver operating characteristic (ROC) analysis, which neglects location information. JAFROC neglects the non-lesion localization marks ("false positives") on abnormal images. JAFROC1 is an alternative method that includes these marks. Both methods are lesion-centric in the sense that they assign equal importance to all lesions; an image with many lesions would tend to dominate the performance metric, and clinically less significant lesions are treated identically as more significant ones. In this paper weighted JAFROC and JAFROC1 analyses are described that treat each abnormal image (not each lesion) as a unit of measurement and account for different lesion clinical significances (weights). Lesion-centric and weighted methods were tested using a simulator that includes multiple-reader multiple-case multiple-modality location level correlations. For comparison, ROC analysis was also tested where the rating of the highest rated mark on an image was assumed to be its "ROC" rating. The testing involved random numbers of lesions per image, random weights, case-mixes (ratio of normal to abnormal images) and different correlation structures. We found that for either JAFROC or JAFROC1, both lesion-centric and weighted analyses had correct NH behavior and comparable statistical powers. For either lesion-centric or weighted analyses JAFROC1 yielded the highest power, followed by JAFROC and ROC yielded the least power, confirming a recent study using a less flexible single-reader dual-modality simulator. Provided the number of normal cases is not too small, JAFROC1 is the preferred method for analyzing human observer free-response data. For either JAFROC or JAFROC1 weighted analysis is preferable.

Proceedings Article | 28 January 2009 Paper

Multiridgelets for texture analysis

Hong-Jun Yoon, Ching-Chung Li

Proceedings Volume 7248, 724803 (2009) https://doi.org/10.1117/12.805626

KEYWORDS: Wavelets, Radon transform, LCDs, Feature extraction, Image processing, Wavelet transforms, Image analysis, Statistical analysis, Image classification, Electronic filtering

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Proceedings Article | 6 March 2008 Paper

Investigation of methods for analyzing location specific observer performance data

D. Chakraborty, Hong-Jun Yoon

Proceedings Volume 6917, 69170C (2008) https://doi.org/10.1117/12.770531

KEYWORDS: Computer aided design, Statistical analysis, Computer simulations, Data modeling, Computer aided diagnosis and therapy, Solid modeling, Imaging systems, Detection and tracking algorithms, Receivers, Interference (communication)

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Proceedings Article | 7 August 2001 Paper

Search engine for remote database-aided interpretation of digitized mammograms

Chester Ornes, Daniel Valentino, Hong-Jun Yoon, Jack Eisenman, Jack Sklansky

Proceedings Volume 4323, (2001) https://doi.org/10.1117/12.435467

KEYWORDS: Databases, Neural networks, Diagnostics, Mammography, Biopsy, Visualization, Human-machine interfaces, Genetic algorithms, Java, Computer aided diagnosis and therapy

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Showing 5 of 17 publications

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