Dr. Bihong Beth Chen Profile

Dr. Bihong Beth Chen

at City of Hope

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SPIE Journal Paper | 8 May 2023

Contributions of global and local processing on medical image perception

Hyung-Bum Park, Lilian Azer, Shinhae Ahn, Tam-Dan Dinh, Gabriela Macias, Gavin Zhang, Bihong Beth Chen, Huiyan Ma, Mahesh Botejue, Eric Choi, Weiwei Zhang

JMI, Vol. 10, Issue S1, S11911, (May 2023) https://doi.org/10.1117/12.10.1117/1.JMI.10.S1.S11911

KEYWORDS: Image processing, Mammography, Signal processing, Medical imaging, Visual process modeling, Data modeling, Signal detection, Visualization, Cancer detection, Modeling

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PurposeThe influential holistic processing hypothesis attributes expertise in medical image perception to cognitive processing of global gist information. However, it has remained unclear whether or how experts use rapid global impression of images for their subsequent diagnostic decisions based on the focal sign of cancer. We hypothesized that continuous-global and discrete-local processes jointly attribute to radiological experts’ detection of mammogram, with different weights and temporal dynamics.ApproachWe examined experienced versus inexperienced observers’ performance at first (500 ms) versus second (2500 ms) mammogram image presentation in an abnormality detection task. We applied a dual-trace signal detection (DTSD) model of receiver operating characteristic (ROC) to assess the time-varying contributions of global and focal cancer signals on mammogram reading and medical expertise.ResultsThe hierarchical Bayesian DTSD modeling of empirical ROCs revealed that mammogram expertise (experienced versus inexperienced observers) manifests largely in a continuous-global component for the detection of the gist of abnormality at the early phase of mammogram reading. For the second presentation of the same mammogram images, the experienced participants showed increased task performance that was largely driven by better processing of discrete-local information, whereas the global processing of abnormality remained saturated from the first exposure. Modeling of the mouse trajectory of the confidence rating responses further revealed the temporal dynamics of global and focal processing.ConclusionsThese results suggest a joint contribution of continuous-global and discrete-local processes on medical expertise, and these processes could be analytically dissociated.

Proceedings Article | 28 February 2020 Presentation + Paper

Inferring brain functional hubs by eigencentrality mapping of phase fMRI connectivity

Zikuan Chen, Qiao Shi, Ebenezer Daniel, Bihong Chen

Proceedings Volume 11317, 1131705 (2020) https://doi.org/10.1117/12.2541495

KEYWORDS: Functional imaging, Brain, Brain mapping, Independent component analysis, Neuroimaging

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Considering brain functional connectivity (FC) as a graph network, we can identify the brain function hub nodes that have the most dense and heavy connections in the network. For a real-valued FC matrix (unsigned connections in a value range [0,1]), we can identify the hub nodes by a new method of eigencentrality mapping, which not only counts for the connections to other nodes but also the other nodes’ centrality values through the eigen decomposition of the FC matrix. In addition, there are two kinds of fMRI data, magnitude and phase, that can be used for brain FC and hub analysis. Although both magnitude and phase fMRI data are generated from the same magnetic source through different transformations, they are different in signal measurements, consequently leading to different inferences. We herein report on brain functional hub analysis by constructing the FC matrix from phase fMRI data and identifying the hub nodes by eigencentrality mapping. In our experiment, we collected a cohort of 160 complex-valued fMRI dataset (consisting of magnitude and phase in pairs), and performed independent component analysis (ICA), FC matrices calculation and FC matrices eigen decomposition; thereby obtained the node eigencentrality values in the largest eigenvalue-associated eigenvector. Our results showed that phase fMRI data analysis could determine the resting-state brain functional hubs primarily in the central subcortex and the posterior brain region (parieto-occipital lobes and cerebellum), which were different from the magnitude-inferred hubs in brain superior region (frontal and parietal lobes).

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