Jae Sohn, MD, MS

Clinical Fellow

Biography

I am interested in the intersection of big data and radiology, specifically natural language processing and cardio-thoracic imaging. As a physician with engineering background, I have been broadly involved in projects that use mathematical techniques to tackle research questions in radiology. We also collaborate with outside teams requiring machine learning support. My work has received media coverage from Washington Post, Times UK, Scientific American, AuntMinnie Newsletter, and others. I enjoy participating in machine learning competitions, recently scoring top 2% in the Kaggle Data Science Bowl for lung cancer detection. I have won the global oncology award from the Stanford Health++ hackathon.

I have co-founded the Big Data in Radiology (BDRAD) research team, now part of UCSF Center for Intelligent Imaging, and mentor undergraduate and medical students who wish to learn about data science projects in radiology. A diverse group of students have joined my team from around the world (given the completely remote nature of the group). Many have won the RSNA trainee research prizes, RSNA certificate of merit awards, and scholarships. They are currently at leading CS PhD programs, medical schools, and data science companies, conducting data science projects in radiology. Interested students should reach out with a CV.

Current research projects span natural language processing, imaging biomarker discovery in chest imaging (lung cancer), deep survival analysis, and integration of machine learning innovations to clinical radiology practice. Examples projects include longitudinal lung nodule tracking & characterization from chest CT, generation of radiology specific word embedding, automated radiology protocoling, automated detection of urgent findings from radiology text report, and prediction of healthcare cost from chest radiographs.

Education

Clinical Fellowship, 06/2021 - Cardiothoracic Imaging, University of California San Francisco (UCSF)
T32 Research Fellowship, 2020 - Big Data in Radiology, University of California San Francisco (UCSF)
Residency, 2020 - Diagnostic Radiology, University of California San Francisco (UCSF)
Intern, 2016 - Transitional Year, Santa Clara Valley Medical Center
MD, 2015 - , Geisel School of Medicine at Dartmouth
BA, 2010 - Applied Math & Statistics, Johns Hopkins University
MS, 2010 - Applied Math & Statistics, Johns Hopkins University

Honors and Awards

RSNA Resident/Fellow Research Grant, 2020
Radiology: In Training Editorial Board Member, 2020
RSNA Certificate of Merit Award (Primary Mentor to Student), 2019
RSNA Margulis Award for Scientific Excellence, 2019
CTSI Resident Research Grant, 2018
RSNA Trainee Research Prize (Primary Mentor to Student), 2018
RSNA Trainee Research Prize (Primary Mentor to Student), 2017
SIR Constantin Cope Medical Student Research Award, 2014
RSNA Certificate of Merit Award, 2011

Publications

Sohn JH, Chillakuru YR, Lee S, Lee AY, Kelil T, Hess CP, Seo Y, Vu T, Joe BN. An Open-Source, Vender Agnostic Hardware and Software Pipeline for Integration of Artificial Intelligence in Radiology Workflow. J Digit Imaging. 2020 May 28.
Schaffter T, Buist DSM, Lee CI, Nikulin Y, Ribli D, Guan Y, Lotter W, Jie Z, Du H, Wang S, Feng J, Feng M, Kim HE, Albiol F, Albiol A, Morrell S, Wojna Z, Ahsen ME, Asif U, Jimeno Yepes A, Yohanandan S, Rabinovici-Cohen S, Yi D, Hoff B, Yu T, Chaibub Neto E, Rubin DL, Lindholm P, Margolies LR, McBride RB, Rothstein JH, Sieh W, Ben-Ari R, Harrer S, Trister A, Friend S, Norman T, Sahiner B, Strand F, Guinney J, Stolovitzky G, Mackey L, Cahoon J, Shen L, Sohn JH, Trivedi H, Shen Y, Buturovic L, Pereira JC, Cardoso JS, Castro E, Kalleberg KT, Pelka O, Nedjar I, Geras KJ, Nensa F, Goan E, Koitka S, Caballero L, Cox DD, Krishnaswamy P, Pandey G, Friedrich CM, Perrin D, Fookes C, Shi B, Cardoso Negrie G, Kawczynski M, Cho K, Khoo CS, Lo JY, Sorensen AG, Jung H. Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms. JAMA Netw Open. 2020 Mar 02; 3(3):e200265.
von Schacky CE, Sohn JH, Liu F, Ozhinsky E, Jungmann PM, Nardo L, Posadzy M, Foreman SC, Nevitt MC, Link TM, Pedoia V. Development and Validation of a Multitask Deep Learning Model for Severity Grading of Hip Osteoarthritis Features on Radiographs. Radiology. 2020 Apr; 295(1):136-145.
Ring NY, diFlorio-Alexander RM, Bond JS, Rosenkranz KM, Cervantes E, Sohn JH, Marotti JD. Papillary and sclerosing lesions of the breast detected and biopsied by MRI: Clinical management, upgrade rate, and association with apocrine metaplasia. Breast J. 2019 05; 25(3):393-400.
Trivedi HM, Panahiazar M, Liang A, Lituiev D, Chang P, Sohn JH, Chen YY, Franc BL, Joe B, Hadley D. Large Scale Semi-Automated Labeling of Routine Free-Text Clinical Records for Deep Learning. J Digit Imaging. 2019 02; 32(1):30-37.
Jaewon Yang, Dookun Park, Jae Ho Sohn, Zhen Jane Wang, Grant T. Gullberg, Youngho Seo. Joint Correction of Attenuation and Scatter Using Deep Convolutional Neural Networks (DCNN) for Time-of-Flight PET. 2018; (11852).
Ding Y, Sohn JH, Kawczynski MG, Trivedi H, Harnish R, Jenkins NW, Lituiev D, Copeland TP, Aboian MS, Mari Aparici C, Behr SC, Flavell RR, Huang SY, Zalocusky KA, Nardo L, Seo Y, Hawkins RA, Hernandez Pampaloni M, Hadley D, Franc BL. A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain. Radiology. 2019 02; 290(2):456-464.
Nam JG, Park S, Hwang EJ, Lee JH, Jin KN, Lim KY, Vu TH, Sohn JH, Hwang S, Goo JM, Park CM. Development and Validation of Deep Learning-based Automatic Detection Algorithm for Malignant Pulmonary Nodules on Chest Radiographs. Radiology. 2019 01; 290(1):218-228.
Trivedi H, Mesterhazy J, Laguna B, Vu T, Sohn JH. Automatic Determination of the Need for Intravenous Contrast in Musculoskeletal MRI Examinations Using IBM Watson's Natural Language Processing Algorithm. J Digit Imaging. 2018 04; 31(2):245-251.
Schernthaner RE, Haroun RR, Nguyen S, Duran R, Sohn JH, Sahu S, Chapiro J, Zhao Y, Radaelli A, van der Bom IM, Mauti M, Hong K, Geschwind JH, Lin M. Characteristics of a New X-Ray Imaging System for Interventional Procedures: Improved Image Quality and Reduced Radiation Dose. Cardiovasc Intervent Radiol. 2018 Mar; 41(3):502-508.
Haider SJA, diFlorio-Alexander R, Lam DH, Cho JY, Sohn JH, Harris R. Prospective Comparison of Diagnostic Accuracy Between Point-of-Care and Conventional Ultrasound in a General Diagnostic Department: Implications for Resource-Limited Settings. J Ultrasound Med. 2017 Jul; 36(7):1453-1460.
Sohn JH, Duran R, Zhao Y, Fleckenstein F, Chapiro J, Sahu S, Schernthaner RE, Qian T, Lee H, Zhao L, Hamilton J, Frangakis C, Lin M, Salem R, Geschwind JF. Validation of the Hong Kong Liver Cancer Staging System in Determining Prognosis of the North American Patients Following Intra-arterial Therapy. Clin Gastroenterol Hepatol. 2017 May; 15(5):746-755.e4.
Sahu S, Schernthaner R, Ardon R, Chapiro J, Zhao Y, Sohn JH, Fleckenstein F, Lin M, Geschwind JF, Duran R. Imaging Biomarkers of Tumor Response in Neuroendocrine Liver Metastases Treated with Transarterial Chemoembolization: Can Enhancing Tumor Burden of the Whole Liver Help Predict Patient Survival? Radiology. 2017 06; 283(3):883-894.
Zhao Y, Duran R, Chapiro J, Sohn JH, Sahu S, Fleckenstein F, Smolka S, Pawlik TM, Schernthaner R, Zhao L, Lee H, He S, Lin M, Geschwind JF. Transarterial Chemoembolization for the Treatment of Advanced-Stage Hepatocellular Carcinoma. J Gastrointest Surg. 2016 12; 20(12):2002-2009.
Fleckenstein FN, Schernthaner RE, Duran R, Sohn JH, Sahu S, Marshall K, Lin M, Gebauer B, Chapiro J, Salem R, Geschwind JF. Renal Cell Carcinoma Metastatic to the Liver: Early Response Assessment after Intraarterial Therapy Using 3D Quantitative Tumor Enhancement Analysis. Transl Oncol. 2016 Oct; 9(5):377-383.
Schernthaner RE, Haroun RR, Duran R, Lee H, Sahu S, Sohn JH, Chapiro J, Zhao Y, Gorodetski B, Fleckenstein F, Smolka S, Radaelli A, van der Bom IM, Lin M, Geschwind JF. Improved Visibility of Metastatic Disease in the Liver During Intra-Arterial Therapy Using Delayed Arterial Phase Cone-Beam CT. Cardiovasc Intervent Radiol. 2016 Oct; 39(10):1429-37.
Fleckenstein FN, Schernthaner RE, Duran R, Sohn JH, Sahu S, Zhao Y, Hamm B, Gebauer B, Lin M, Geschwind JF, Chapiro J. 3D Quantitative tumour burden analysis in patients with hepatocellular carcinoma before TACE: comparing single-lesion vs. multi-lesion imaging biomarkers as predictors of patient survival. Eur Radiol. 2016 Sep; 26(9):3243-52.
Tacher V, Duran R, Lin M, Sohn JH, Sharma KV, Wang Z, Chapiro J, Gacchina Johnson C, Bhagat N, Dreher MR, Schäfer D, Woods DL, Lewis AL, Tang Y, Grass M, Wood BJ, Geschwind JF. Multimodality Imaging of Ethiodized Oil-loaded Radiopaque Microspheres during Transarterial Embolization of Rabbits with VX2 Liver Tumors. Radiology. 2016 Jun; 279(3):741-53.
Tacher V, Lin M, Duran R, Yarmohammadi H, Lee H, Chapiro J, Chao M, Wang Z, Frangakis C, Sohn JH, Maltenfort MG, Pawlik T, Geschwind JF. Comparison of Existing Response Criteria in Patients with Hepatocellular Carcinoma Treated with Transarterial Chemoembolization Using a 3D Quantitative Approach. Radiology. 2016 Jan; 278(1):275-84.
Chockalingam A, Duran R, Sohn JH, Schernthaner R, Chapiro J, Lee H, Sahu S, Nguyen S, Geschwind JF, Lin M. Radiologic-pathologic analysis of quantitative 3D tumour enhancement on contrast-enhanced MR imaging: a study of ROI placement. Eur Radiol. 2016 Jan; 26(1):103-13.
Schernthaner RE, Chapiro J, Sahu S, Withagen P, Duran R, Sohn JH, Radaelli A, van der Bom IM, Geschwind JF, Lin M. Feasibility of a Modified Cone-Beam CT Rotation Trajectory to Improve Liver Periphery Visualization during Transarterial Chemoembolization. Radiology. 2015 Dec; 277(3):833-41.
Ng T, Ryu JR, Sohn JH, Tan T, Song H, Ming GL, Goh EL. Class 3 semaphorin mediates dendrite growth in adult newborn neurons through Cdk5/FAK pathway. PLoS One. 2013; 8(6):e65572.
Cheung YY, Jung B, Sohn JH, Ogrinc G. Quality initiatives: statistical control charts: simplifying the analysis of data for quality improvement. Radiographics. 2012 Nov-Dec; 32(7):2113-26.
Sohn JH, Han MJ, Lee MY, Kang SK, Yang JS. Simultaneous determination of N-hydroxymethyl-N-methylformamide, N-methylformamide and N-acetyl-S-(N-methylcarbamoyl)cystein in urine samples from workers exposed to N,N-dimethylformamide by liquid chromatography-tandem mass spectrometry. J Pharm Biomed Anal. 2005 Feb 07; 37(1):165-70.