Honors & Awards

  • School of Medicine Dean's Postdoctoral Fellowship, Stanford University (2024 - 2025)

Professional Education

  • Doctor of Philosophy, University of Rochester, Electrical and Computer Engineering (2023)

Stanford Advisors

Contact

  • Academic
    baekjh@stanford.edu
    University - Scholar Department: Radiology Position: Postdoctoral Scholar

Additional Info

All Publications

  • 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

https://orcid.org/0000-0002-7140-4390