Mr. Folami T. Alamudun Profile

Folami T. Alamudun

at Texas A&M Univ

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Publications (4)

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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