Jihye Baek

All Publications

• Enhancing Ultrasound Molecular Imaging: RPCA-Based Filtering to Differentiate Tumor-Bound and Free Microbubbles Hashemi, H. S., Hyun, D., Baek, J., Natarajan, A., Tabesh, F., Paulmurugan, R., Dahl, J. J., IEEE IEEE. 2024

  View details for DOI 10.1109/UFFC-JS60046.2024.10793633

  View details for Web of Science ID 001428150100142

• A multi-parametric model for progression of metabolic dysfunction-associated steatohepatitis (MASH) in humans Baek, J., Sanabria, S., Oyarzabal, I., Echevarria-Uraga, J. J., Quesada, C., Dahl, J., Parker, K. J., IEEE IEEE. 2024

  View details for DOI 10.1109/UFFC-JS60046.2024.10793524

  View details for Web of Science ID 001428150100042

• Differentiable Beamforming for Distributed Attenuation Estimation and Spatial Gain Compensation (SGC) Frey, B. N., Hyun, D., Simson, W., Brevett, T., Zhuang, L., Baek, J., Sanabria, S. J., Dahl, J. J., IEEE IEEE. 2024

  View details for DOI 10.1109/UFFC-JS60046.2024.10794091

  View details for Web of Science ID 001428150100552

• Frequency estimator to improve H-scan tissue characterization Baek, J., Brevett, T., Hyun, D., El Kaffas, A., Parker, K. J., Dahl, J., IEEE IEEE. 2024

  View details for DOI 10.1109/UFFC-JS60046.2024.10793751

  View details for Web of Science ID 001428150100216

• Nondestructive ultrasound molecular imaging based on a neural network approach utilizing post-processed ultrasound images Baek, J., Hyun, D., Nataraj, A., Tabesh, F., Paulmurugan, R., Dahl, J. J., IEEE IEEE. 2024

  View details for DOI 10.1109/UFFC-JS60046.2024.10793498

  View details for Web of Science ID 001428150100019

• Improving breast cancer diagnosis by incorporating raw ultrasound parameters into machine learning MACHINE LEARNING-SCIENCE AND TECHNOLOGY Baek, J., O'Connell, A. M., Parker, K. J. 2022; 3 (4): 045013

  Abstract

  The improved diagnostic accuracy of ultrasound breast examinations remains an important goal. In this study, we propose a biophysical feature-based machine learning method for breast cancer detection to improve the performance beyond a benchmark deep learning algorithm and to furthermore provide a color overlay visual map of the probability of malignancy within a lesion. This overall framework is termed disease-specific imaging. Previously, 150 breast lesions were segmented and classified utilizing a modified fully convolutional network and a modified GoogLeNet, respectively. In this study multiparametric analysis was performed within the contoured lesions. Features were extracted from ultrasound radiofrequency, envelope, and log-compressed data based on biophysical and morphological models. The support vector machine with a Gaussian kernel constructed a nonlinear hyperplane, and we calculated the distance between the hyperplane and each feature's data point in multiparametric space. The distance can quantitatively assess a lesion and suggest the probability of malignancy that is color-coded and overlaid onto B-mode images. Training and evaluation were performed on in vivo patient data. The overall accuracy for the most common types and sizes of breast lesions in our study exceeded 98.0% for classification and 0.98 for an area under the receiver operating characteristic curve, which is more precise than the performance of radiologists and a deep learning system. Further, the correlation between the probability and Breast Imaging Reporting and Data System enables a quantitative guideline to predict breast cancer. Therefore, we anticipate that the proposed framework can help radiologists achieve more accurate and convenient breast cancer classification and detection.

  View details for DOI 10.1088/2632-2153/ac9bcc

  View details for Web of Science ID 000880819300001

  View details for PubMedID 36698865

  View details for PubMedCentralID PMC9855672