J
Jae Ho Sohn
Researcher at University of California, San Francisco
Publications - 49
Citations - 1896
Jae Ho Sohn is an academic researcher from University of California, San Francisco. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 16, co-authored 44 publications receiving 1029 citations. Previous affiliations of Jae Ho Sohn include Johns Hopkins University School of Medicine & University of California, Berkeley.
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Journal ArticleDOI
A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain.
Yiming Ding,Yiming Ding,Jae Ho Sohn,Michael Kawczynski,Hari Trivedi,Roy Harnish,Nathaniel W. Jenkins,Dmytro Lituiev,Timothy P. Copeland,Mariam Aboian,Carina Mari Aparici,Spencer C. Behr,Robert R. Flavell,Shih-ying Huang,Kelly A. Zalocusky,Lorenzo Nardo,Youngho Seo,Randall A. Hawkins,Miguel Hernandez Pampaloni,Dexter Hadley,Benjamin L. Franc +20 more
TL;DR: A deep learning algorithm developed for early prediction of Alzheimer disease achieved 82% specificity at 100% sensitivity, an average of 75.8 months prior to the final diagnosis, which in ROC space outperformed reader performance.
Journal ArticleDOI
Development and Validation of Deep Learning-based Automatic Detection Algorithm for Malignant Pulmonary Nodules on Chest Radiographs.
Ju Gang Nam,Sunggyun Park,Eui Jin Hwang,Jong Hyuk Lee,Kwang Nam Jin,Kun Young Lim,Thienkai Huy Vu,Jae Ho Sohn,Sangheum Hwang,Jin Mo Goo,Chang Min Park +10 more
TL;DR: This deep learning-based automatic detection algorithm outperformed physicians in radiograph classification and nodule detection performance for malignant pulmonary nodules on chest radiographs, and it enhanced physicians' performances when used as a second reader.
Journal ArticleDOI
Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms
Thomas Schaffter,Diana S. M. Buist,Christoph I. Lee,Yaroslav Nikulin,Dezső Ribli,Yuanfang Guan,William Lotter,Zequn Jie,Hao Du,Sijia Wang,Jiashi Feng,Mengling Feng,Hyo-Eun Kim,F. Albiol,Alberto Albiol,Stephen Morrell,Zbigniew Wojna,Mehmet Eren Ahsen,Umar Asif,Antonio Jimeno Yepes,Shivanthan A.C. Yohanandan,Simona Rabinovici-Cohen,Darvin Yi,Bruce Hoff,Thomas Yu,Elias Chaibub Neto,Daniel L. Rubin,Peter Lindholm,Laurie R. Margolies,Russell B. McBride,Joseph H. Rothstein,Weiva Sieh,Rami Ben-Ari,Stefan Harrer,Andrew D. Trister,Stephen H. Friend,Thea Norman,Berkman Sahiner,Fredrik Strand,Fredrik Strand,Justin Guinney,Gustavo Stolovitzky,Lester Mackey,Joyce Cahoon,Li Shen,Jae Ho Sohn,Hari Trivedi,Yiqiu Shen,Ljubomir Buturovic,Jose Costa Pereira,Jaime S. Cardoso,Eduardo Castro,Karl Trygve Kalleberg,Obioma Pelka,Imane Nedjar,Krzysztof J. Geras,Felix Nensa,Ethan Goan,Sven Koitka,Sven Koitka,Luis Caballero,David D. Cox,Pavitra Krishnaswamy,Gaurav Pandey,Christoph M. Friedrich,Dimitri Perrin,Clinton Fookes,Bibo Shi,Gerard Cardoso Negrie,Michael Kawczynski,Kyunghyun Cho,Can Son Khoo,Joseph Y. Lo,A. Gregory Sorensen,Hwejin Jung +74 more
TL;DR: This diagnostic accuracy study evaluates whether artificial intelligence can overcome human mammography interpretation limits with a rigorous, unbiased evaluation of machine learning algorithms.
Journal ArticleDOI
Federated learning for predicting clinical outcomes in patients with COVID-19.
Ittai Dayan,Holger R. Roth,Aoxiao Zhong,Ahmed Harouni,Amilcare Gentili,Anas Z. Abidin,Andrew Liu,Anthony Costa,Bradford J. Wood,Chien-Sung Tsai,Chih-Hung Wang,Chun-Nan Hsu,C. K. Lee,Peiying Ruan,Daguang Xu,Dufan Wu,Eddie Huang,Felipe Kitamura,Griffin Lacey,Gustavo César de Antônio Corradi,Gustavo Nino,Hao-Hsin Shin,Hirofumi Obinata,Hui Ren,Jason C. Crane,Jesse Tetreault,Jiahui Guan,John Garrett,Joshua D. Kaggie,Jung Gil Park,Keith J. Dreyer,Krishna Juluru,Kristopher Kersten,Marcio Aloisio Bezerra Cavalcanti Rockenbach,Marius George Linguraru,Marius George Linguraru,Masoom A. Haider,Masoom A. Haider,Meena AbdelMaseeh,Nicola Rieke,Pablo F. Damasceno,Pedro Mário Cruz e Silva,Pochuan Wang,Sheng Xu,Shuichi Kawano,Sira Sriswasdi,Soo-Young Park,Thomas M. Grist,Varun Buch,Watsamon Jantarabenjakul,Watsamon Jantarabenjakul,Weichung Wang,Won Young Tak,Xiang Li,Xihong Lin,Young Joon Kwon,Abood Quraini,Andrew Feng,Andrew N. Priest,Baris Turkbey,Benjamin S. Glicksberg,Bernardo Bizzo,Byung Seok Kim,Carlos Tor-Díez,Chia-Cheng Lee,Chia-Jung Hsu,Chin Lin,Chiu-Ling Lai,Christopher P. Hess,Colin B. Compas,Deepeksha Bhatia,Eric K. Oermann,Evan Leibovitz,Hisashi Sasaki,Hitoshi Mori,Isaac Yang,Jae Ho Sohn,Krishna Nand Keshava Murthy,Li-Chen Fu,Matheus Ribeiro Furtado de Mendonça,Mike Fralick,Min Kyu Kang,Mohammad Adil,Natalie Gangai,Peerapon Vateekul,Pierre Elnajjar,Sarah E Hickman,Sharmila Majumdar,Shelley McLeod,Sheridan Reed,Stefan Gräf,Stephanie Harmon,Tatsuya Kodama,Thanyawee Puthanakit,Thanyawee Puthanakit,Tony Mazzulli,Tony Mazzulli,Vitor Lavor,Yothin Rakvongthai,Yu Rim Lee,Yuhong Wen,Fiona J. Gilbert,Mona Flores,Quanzheng Li +103 more
TL;DR: In this article, the authors used federated learning to predict future oxygen requirements of symptomatic patients with COVID-19 using inputs of vital signs, laboratory data and chest X-rays.
Journal ArticleDOI
Comparison of Existing Response Criteria in Patients with Hepatocellular Carcinoma Treated with Transarterial Chemoembolization Using a 3D Quantitative Approach
Vania Tacher,MingDe Lin,Rafael Duran,Hooman Yarmohammadi,Howard Lee,Julius Chapiro,Michael Chao,Zhijun Wang,Constantine Frangakis,Jae Ho Sohn,Mitchell Gil Maltenfort,Timothy M. Pawlik,Jean Francois H. Geschwind +12 more
TL;DR: 3D-based imaging biomarkers qEASL and vRECIST were tumor response criteria that could be used to predict patient survival early after initial TACE and enabled clear identification of nonresponders.