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Publication

2023
2022
2021
2020
2019
2018
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
2005
2004

2023

  • Chung, J., Kim, D., Choi, J., Yune, S., Song, K. D., Kim, S., Chua, M., Succi, M. D., Conklin, J., Longo, M. G. F., Ackman, J. B., Petranovic, M., Lev, M. H., & Do, S. “Prediction of oxygen requirement in patients with COVID-19 using a pre-trained chest radiograph xAI model: efficient development of auditable risk prediction models via a fine-tuning approach”. Scientific reports, 13(1), 4296 (2023).
  • Yoon, Byung C., Pomerantz, Stuart R., Mercaldo, Nathaniel D., Goyal, Swati, L’Italien, Eric M., Lev, Michael H., Buch, Karen A., Buchbinder, Bradley R., Chen, John W., Conklin, John, Gupta, Rajiv AND Hunter, George J., Kamalian, Shahmir C., Kelly, Hillary R., Rapalino, Otto, Rincon, Sandra P., Romero, Javier M., He, Julian, Schaefer, Pamela W., Do, Synho, González, Ramon. “Incorporating algorithmic uncertainty into a clinical machine deep learning algorithm for urgent head CTs”.PLOS ONE 18, no.3 (2023): 1-15.
  • Bahl, Manisha, and Synho, Do. “Beyond the AJR: An International Competition Advances Artificial Intelligence Research”.AJR. American journal of roentgenology (2023).
  • Bahl, Manisha, and Synho, Do. “Artificial Intelligence Applied to Contrast-enhanced Mammography: Exploring Uncharted Territory”.Radiology 307, no.5 (2023): e231140.

2022

  • Kim, Doyun, Joowon Chung, Jongmun Choi, Marc D. Succi, John Conklin, Maria Gabriela Figueiro Longo, Jeanne B. Ackman, Brent P. Little, Milena Petranovic, Mannudeep K. Kalra, Michael H. Lev, and Synho Do. “Accurate auto-labeling of chest X-ray images based on quantitative similarity to an explainable AI model.” Nature Communications 13, no. 1 (2022): 1867.
  • Jongmun Choi, , Soomin Jeon, Doyun Kim, Michelle Chua, and Synho Do. “A scalable artificial intelligence platform that automatically finds copy number variations (CNVs) in journal articles and transforms them into a database: CNV extraction, transformation, and loading AI (CNV-ETLAI)”.Computers in Biology and Medicine 144 (2022): 105332.
  • Zhang, Mingjuan L., Jongmun Choi, Richard Judelson, Soomin Jeon, Deepa Patil, Vikram Deshpande, and Synho Do. “Small and Efficient Artificial Intelligence Model Can Differentiate Hyperplastic Polyps and Sessile Serrated Adenomas/Polyps.” Gastroenterology 162 (7): S701-S701 (2022).
  • Joowon Chung, Doyun Kim, Jongmun Choi, Sehyo Yune, Kyoung Doo Song, Seonkyoung Kim, Michelle Chua, Marc D. Succi, John Conklin, Maria G. Figueiro Longo, Jeanne B. Ackman, Milena Petranovic, Michael H. Lev and Synho Do. “Prediction of oxygen requirement in patients with COVID-19 using a pre-trained chest radiograph xAI model: efficient development of auditable risk prediction models via a fine-tuning approach”. Sci Rep12, 21164 (2022).
  • Michelle Chua, Doyun Kim, Jongmun Choi, Michael H. Lev, Ramon G. Gonzalez, Michael S. Gee and Synho Do. “Tackling prediction uncertainty in machine learning for healthcare”. Nat. Biomed. Eng 7, 711–718 (2023).

2021

  • Witowski, Jan, Jongmun, Choi, Soomin, Jeon, Doyun, Kim, Joowon, Chung, John, Conklin, Maria Gabriela Figueiro, Longo, Marc D, Succi, and Synho, Do. “MarkIt: A Collaborative Artificial Intelligence Annotation Platform Leveraging Blockchain For Medical Imaging Research”.Blockchain Healthc Today 4 (2021).
  • Doyun, Kim, Joowon, Chun, Jongnum, Choi, Marc, Succi, John, Conklin, Maria, Figueiro, Jeanne, Ackman, Brent, Little, Milena Petranovic, Mannudeep, Kalra, Muchael, Lev, Synho, Do. “Automated Labeling of Chest X-ray Images using a Combination of Deep Learning and Radiologist Expertise.” Research Square, (2023).

2020

  • Sim, Yongsik, Myung Jin, Chung, Elmar, Kotter, Sehyo, Yune, Myeongchan, Kim, Synho, Do, Kyunghwa, Han, Hanmyoung, Kim, Seungwook, Yang, Dong-Jae, Lee, and Byoung Wook, Choi. “Deep Convolutional Neural Network–based Software Improves Radiologist Detection of Malignant Lung Nodules on Chest Radiographs”.Radiology 294, no.1 (2020): 199-209.
  • Synho, Do, Song, Kyoung Doo, Chung, Joo Won . “Basics of Deep Learning: A Radiologist’s Guide to Understanding Published Radiology Articles on Deep Learning”.kjr 21, no.1 (2020): 33-41.

2019

  • Tajmir, Shahein H., Hyunkwang Lee, Randheer Shailam, Heather I. Gale, Jie C. Nguyen, Sjirk J. Westra, Ruth Lim, Sehyo Yune, Michael S. Gee, and Synho Do. “Artificial intelligence-assisted interpretation of bone age radiographs improves accuracy and decreases variability.” Skeletal Radiology 48, no. 2 (2019): 275-283.
  • Parakh, Anushri, Hyunkwang Lee, Jeong Hyun Lee, Brian H. Eisner, Dushyant V. Sahani, and Synho Do. “Urinary Stone Detection on CT Images Using Deep Convolutional Neural Networks: Evaluation of Model Performance and Generalization.” Radiology: Artificial Intelligence 1, no. 4 (2019): e180066.
  • Yune, Sehyo, Hyunkwang Lee, Myeongchan Kim, Shahein H. Tajmir, Michael S. Gee, and Synho Do. “Beyond Human Perception: Sexual Dimorphism in Hand and Wrist Radiographs Is Discernible by a Deep Learning Model.” Journal of digital imaging 32, no. 4 (2019): 665-671.
  • Song, Kyoung Doo, Myeongchan Kim, and Synho Do. “The Latest Trends in the Use of Deep Learning in Radiology Illustrated Through the Stages of Deep Learning Algorithm Development.” Journal of the Korean Society of Radiology 80, no. 2 (2019): 202-212.
  • Muelly, Michael C., and Lily Peng. “Spotting brain bleeding after sparse training.” Nature Biomedical Engineering 3, no. 3 (2019): 161. (Worth to read, Commentary for our “Explainable AI”  paper from Google Cloud Team.)

2018

  • Lee, H., Yune, S., Mansouri, M., Kim, M., Tajmir, S., Guerrier, C., Ebert, S., Pomerantz, S., Romero, J., Kamalian, S., Gonzalez, R., Lev, M., Do, S. An explainable deep-learning algorithm for the detection of acute intracranial haemorrhage from small datasets. Nature Biomedical Engineering, Pp. 1-10. 12/17/2018.
  • Lee, H., Kim, M., Do, S. Practical Window Setting Optimization for Medical Image Deep Learning. Machine Learning for Health (ML4H) Workshop at NeurIPS 12/3/2018.
  • Yune, S.*, Lee, H.*, Kim, M., Tajmir, S., Gee, M., Do, S. Beyond Human Perception: Sexual Dimorphism in Hand and Wrist Radiographs Is Discernible by a Deep-Learning Model. Journal of Digital Imaging. 11/26/2018.
  • Tajmir, S., Lee, H., Shailam, R., Gale, H., Nguyen, J., Westra, S., Lim, R., Yune, S., Gee, M., Do, S. Artificial intelligence-assisted interpretation of bone age radiographs improves accuracy and decreases variability. Skeletal Radiology, Pp. 1-9. 8/1/2018.

2017

  • Lee, H., Troschel, F.M., Tajmir, S. et al. Pixel-Level Deep Segmentation: Artificial Intelligence Quantifies Muscle on Computed Tomography for Body Morphometric Analysis. J Digit Imaging (2017). doi:10.1007/s10278-017-9988-z.
  • Cho J, Lee E, Lee H, Liu B, Li X, Tajmir S, Sahani D, Do S. Machine Learning Powered Automatic Organ Classification for Patient Specific Organ Dose Estimation. Society for Imaging Informatics in Medicine. Vol 2017. ; 2017.
  • Lee H, Rogers J, Cho J, Daye D, Mishra V, Choy G, Tajmir S, Lev M, Do S. Machine Intelligence for Accurate X-ray Screening and Read-out Prioritization: PICC line Detection Study. Society for Imaging Informatics in Medicine. Vol 2017. Pittsburgh, PA ; 2017.
  • Puchner SB, Ferencik M, Maehara A, Stolzmann P, Ma S, Do S, Kauczor H-U, Mintz GS, Hoffmann U, Schlett CL. Iterative Image Reconstruction Improves the Accuracy of Automated Plaque Burden Assessment in Coronary CT Angiography: A Comparison With Intravascular Ultrasound . American Journal of Roentgenology. 2017;2018 :1-8.
  • Lee H, Tajmir S, Lee J, Zissen M, Yeshiwas BA, Alkasab TK, Choy G, Do S. Fully Automated Deep Learning System for Bone Age Assessment. Journal of Digital Imaging. 2017;2017 :1-15.
  • Leonardo I. Valentin MD, Colin McCarthy MD, Synho Do PD, Efren Flores MD, Raul Uppot MD. Predicting multidisciplinary tumor board recommendations: Initial experience with machine learning in interventional oncology. Journal of Vascular and Interventional Radiology [Internet]. 2017;28 (2) :S19-S20.

2016

  • Do S. The future of artificial intelligence for physicians (인공지능과 의사의 미래). J Korean Med Assoc [Internet]. 2016;59 (6) :410-412.

2015

  • Padole AMD, Singh SMD, Lira DMD, Blake MAMD, Pourjabbar SMD, Khawaja RDAMD, Choy, Garry MD MBA, Saini SMD, Do SPD, Kalra MKMD. Assessment of Filtered Back Projection, Adaptive Statistical, and Model-Based Iterative Reconstruction for Reduced Dose Abdominal Computed Tomography. Journal of Computer Assisted Tomography. 2015;39 (4) :462-467.
  • Cho J, Lee K, Shin E, Choy G, Do S. How much data is needed to train a medical image deep learning system to achieve necessary high accuracy?. arXiv.org [Internet]. 2015. Publisher’s VersionAbstract
  • Khawaja RDA, Singh S, Blake M, Harisinghani M, Choy G, Karosmangulu A, Padole A, Do S, Brown K, Thompson R, et al. Ultra-low dose abdominal MDCT: Using a knowledge-based Iterative Model Reconstruction technique for substantial dose reduction in a prospective clinical study. European Journal of Radiology. 2015;84 (1) :2-10.
  • Khawaja RDAMD, Singh, Sarabjeet MD MMST, Blake MMD, Harisinghani MMD, Choy GMD, Karosmanoglu AMD, Padole AMD, Pourjabbar SMD, Do SPD, Kalra MKMD. Ultralow-Dose Abdominal Computed Tomography: Comparison of 2 Iterative Reconstruction Techniques in a Prospective Clinical Study. Journal of Computer Assisted Tomography. 2015;39 (4) :489-498.

2014

  • Do S, Pourjabbar S, Khawaja R, Padole A, Singh S, Kalra M. Texturization: A Generalized Image Quality Comparison Method, in The Third International Conference on Image Formation in X-ray Computed Tomography. Vol 3. Salt Lake City, UT: University of Utah ; 2014.
  • Do S, Karl WC, Singh S, Kalra M, Brady T, Shin E, Pien H. High Fidelity System Modeling for High Quality Image Reconstruction in Clinical CT. PloS one. 2014;9 (11) :e111625.
  • Padole A, Singh S, Ackman JB, Wu C, Do S, Pourjabbar S, Khawaja RDA, Otrakji A, Digumarthy S, Shepard J-A. Submillisievert Chest CT With Filtered Back Projection and Iterative Reconstruction Techniques. American Journal of Roentgenology. 2014;203 (4) :772-781.
  • Khawaja RDA, Singh S, Blake M, Harisinghani M, Choy G, Karosmangulu A, Padole A, Do S, Brown K, Thompson R. Ultra-low dose abdominal MDCT: Using a knowledge-based Iterative Model Reconstruction technique for substantial dose reduction in a prospective clinical study. European journal of radiology. 2014;84 (1) :2-10.
  • Do S, Karl CW. Sinogram Sparsified Metal Artifact Reduction Technology (SSMART). The Third International Conference on Image Formation in X-ray Computed Tomography. 2014;3.
  • Khawaja RDA, Singh S, Gilman M, Sharma A, Do S, Pourjabbar S, Padole A, Lira D, Brown K, Shepard J-AO, et al.
  • Computed Tomography (CT) of the Chest at Less Than 1 mSv: An Ongoing Prospective Clinical Trial of Chest CT at Submillisievert Radiation Doses with Iterative Model Image Reconstruction and iDose 4 Technique. Journal of Computer Assisted Tomography. 2014;38 :613-619.
  • Ando M, Sunaguchi N, Wu Y, Do S, Sung Y, Louissaint A, Yuasa T, Ichihara S, Gupta R. Crystal analyser-based X-ray phase contrast imaging in the dark field: implementation and evaluation using excised tissue specimens. European Radiology. 2014;24 (2) :423-433.
  • Pourjabbar S, Singh S, Kulkarni N, Muse V, Digumarthy SR, Khawaja RDA, Padole A, Do S, Kalra MK. Dose reduction for chest CT: Comparison of two iterative reconstruction techniques. ACTA RADIOLOGICA. 2014.
  • Pourjabbar S, Singh S, Singh AK, Johnston RP, Shenoy-Bhangle AS, Do S, Padole A, Blake MA, Persson A, Kalra MK. Prelimineary Results: Prospective Clinical Study to Assess Image-based Iterative Reconstruction for Abdominal computed tomography acquired at 2 radiation dose levels. Journal of Computer Assisted Tomography. 2014;38 (1) :117-122.
  • Deedar Khawaja RA, Singh S, Lira D, Bippus R, Do S, Padole A, Pourjabbar S, Koehler T, Shepard J-A, Kalra MK. Role of Compressive Sensing Technique in Dose reduction for Chest Computed Tomography: A Prospective Blinded Clinical Study. Journal of Computer Assisted Tomography. 2014;00 (00) :1-8.

2013

  • Lee S, Shima A, Singh S, Kalra MK, Kim H-J, Do S. Co-registered image quality comparison in hybrid iterative reconstruction techniques: SAFIRE and SafeCT, in SPIE Medical Imaging. ; 2013 :86683G–86683G.
  • Näppi JJ, Do S, Yoshida H. Computer-Aided Detection of Colorectal Lesions with Super-Resolution CT Colonography: Pilot Evaluation. In: Abdominal Imaging. Computation and Clinical Applications. Springer Berlin Heidelberg ; 2013. pp. 73–80.
  • Do S, Näppi JJ, Yoshida H. Iterative Reconstruction for Ultra-Low-Dose Laxative-Free CT Colonography. In: Abdominal Imaging. Computation and Clinical Applications. Springer Berlin Heidelberg ; 2013. pp. 99–106.
  • Schlett CL, Ferencik M, Celeng C, Maurovich-Horvat Pál, Scheffel H, Stolzmann P, Do S, Kauczor H-U, Alkadhi H, Bamberg F. How to assess non-calcified plaque in CT angiography: delineation methods affect diagnostic accuracy of low-attenuation plaque by CT for lipid-core plaque in histology. European Heart Journal–Cardiovascular Imaging %@ 2047-2404. 2013.
  • Ando M, Sunaguchi N, Wu Y, Do S, Sung Y, Louissaint A, Yuasa T, Ichihara S, Gupta R. , Crystal analyser-based X-ray phase contrast imaging in the dark field: implementation and evaluation using excised tissue specimens. European Radiology. 2013;( 10.1007/s00330-013-3021-9) :1-11.
  • Liew G, Ali N, Do S, Petranovic M, Cury R, Brady T, Hoffmann U, Ghoshhajra B. A Novel Analysis Algorithm for Potential Quantitative Assessment of Myocardial Computed Tomography Perfusion. Academic Radiology. 2013;20 (10) :1301-1306.
  • Puchner SB, Ferencik M, Karolyi M, Do S, Maurovich-Horvat P, Kauczor H-U, Hoffmann U, Schlett CL. The effect of iterative image reconstruction algorithms on the feasibility of automated plaque assessment in coronary CT angiography. The International Journal of Cardiovascular Imaging. 2013;(.1007/s10554-013-0281-z) :1-10.
  • Schlett CL, l Maurovich-Horvat P´, Ferencik M, Alkadhi H, Stolzmann P, Scheffel H, Seifarth H, Nakano M, Do S, Vorpahl M, et al. Histogram Analysis of Lipid-Core Plaques in Coronary Computed Tomographic Angiography: Ex Vivo Validation Against Histology. Investigative Radiology. 2013;48 (9) :646-653.
  • Kalra MK, Woisetschläger M, Dahlström N, Singh S, Digumarthy S, Do S, Pien H, Quick P, Schmidt B, Sedlmair M, et al. Sinogram-Affirmed Iterative Reconstruction of Low-Dose Chest CT: Effect on Image Quality and Radiation Dose. American Journal of Roentgenology. 2013;201 (2) :235-244.

2012

  • Stojanovic I, Pien H, Do S, Karl CW. Low-dose X-ray CT reconstruction based on joint sinogram smoothing and learned dictionary-based representation, in Biomedical Imaging (ISBI), 2012 9th IEEE International Symposium on. ; 2012 :1012-1015.
  • Singh S, Kalra MK, Do S, Thibault JB, Pien H, Connor OOJ, Blake M. Comparison of Hybrid and Pure Iterative Reconstruction Techniques with Conventional Filtered Back Projection: Dose Reduction Potential in the Abdomen. Journal of Computer Assisted Tomography. 2012;36 (3) :347-353.
  • Ghoshhajra BB, Engel L-C, Major GP, Goehler A, Techasith T, Verdini D, Do S, Liu B, Li X, Sala M, et al. Evolution of Coronary Computed Tomography Radiation Dose Reduction at a Tertiary Referral Center. The American Journal of Medicine. 2012;125 (8) :764-772.
  • Do S, Salvaggio K, Gupta S, Kalra M, Pien H. Automated Quantification of Pneumothorax in CT. Computational and Mathematical Methods in Medicine. 2012;2012 (10.1155/2012/736320).
  • Scheffel H, Stolzmann P, Schlett CL, Engel L-C, Major GP, Károlyi M, Do S, Maurovich-Horvat Pál, Hoffmann U. Coronary Artery Plaques: Cardiac CT with Model-Based and Adaptive-Statistical Iterative Reconstruction Technique. European Journal of Radiology. 2012;81 (3) :363-369.
  • Pien H, Do S, Singh S, Kalra MK. Conventional and Newer Reconstruction Techniques in CT. In: Radiation Dose from Multidetector CT. Springer Berlin Heidelberg ; 2012. pp. 143-156.

2011

  • Eger L, Do S, Ishwar P, Karl CW, Pien H. A learning-based approach to explosives detection using multi-energy x-Ray computed tomography, in Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on. IEEE ; 2011 :2004-2007.
  • Do S. A novel hybrid algorithm for accelerating CT reconstructions and improving low-dose image quality, in 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. ; 2011 :1516-1519.
  • Do S, Karl CW, Liang Z, Kalra M, Brady TJ, Pien HH. A Decomposition-based CT Reconstruction Formulation for Reducing Blooming Artifacts. Physics in Medicine and Biology. 2011;56 :7109-7125.
  • Singh S, Kalra MK, Hsieh J, Licato PE, Do S, Pien HH, Blake MA. Abdominal CT: Comparison of Adaptive Statistical Iterative and Filtered Back Projection Reconstruction Techniques. Radiology. 2011;257 :373-383.

2010

  • Do S, Karl CW, Kalra MK, Brady TJ, Pien H. A variational approach for reconstructing low dose images in clinical helical CT, in Biomedical Imaging: From Nano to Macro, 2010 IEEE International Symposium on. IEEE ; 2010 :784-787.
  • He L, Orten B, Do S, Karl CW, Kambadakone A, Sahani DV, Pien H. A Spatio-temporal Deconvolution Method to Improve Perfusion CT Quantification. IEEE Transactions on Medical Imaging. 2010;29 (5) :1182-1191.
  • Prakash P, Kalra MK, Ackman JB, Digumarthy SR, Hsieh J, Do S, Shepard J-AO, Gilman MD. Diffuse Lung Disease: CT of the Chest with Adaptive Statistical Iterative Reconstruction Technique. Radiology. 2010;256 :261-269.

2009

  • Do S, Cho S, Karl CW, Kalra MK, Brady TJ, Pien H. Accurate model-based high resolution cardiac image reconstruction in dual source CT, in Biomedical Imaging: From Nano to Macro, 2009. ISBI’09. IEEE International Symposium on. IEEE ; 2009 :330-333.
  • Jang B, Kaeli D, Do S, Pien H. Multi GPU implementation of iterative tomographic reconstruction algorithms, in Biomedical Imaging: From Nano to Macro, 2009. ISBI’09. IEEE International Symposium on. IEEE ; 2009 :185-188.
  • He L, Orten BB, Do S, Karl CW, Kambadakone A, Sahani DV, Pien H. Spatio-temporal deconvolution of perfusion CT data in rectal tumor patients, in Biomedical Imaging: From Nano to Macro, 2009. ISBI’09. IEEE International Symposium on. IEEE ; 2009 :1231-1234.

2008

  • Liang Z, Karl CW, Do S, Brady T, Pien H. Analysis and mitigation of calcium artifacts in cardiac multidetector CT, in Biomedical Imaging: From Nano to Macro, 2008. ISBI 2008. 5th IEEE International Symposium on. IEEE ; 2008 :1477-1480.
  • Jeong J-W, Shin DC, Do S, Blanco C, Klipfel NE, Holmes DR, Hovanessian-Larsen LJ, Marmarelis VZ. Differentiation of cancerous lesions in excised human breast specimens using multiband attenuation profiles from ultrasonic transmission tomography. Journal of Ultrasound in Medicine. 2008;27 :435-451.

2007

  • Marmarelis V, Jeong J-W, Shin DC, Do S. High-resolution 3-D Imaging and Tissue Differentiation with Transmission Tomography. Acoustical Imaging. 2007;3 :185-206.

2006

  • Jeong J-W, Shin DC, Do S, Marmarelis VZ. Segmentation methodology for automated classification and differentiation of soft tissues in multiband images of high-resolution ultrasonic transmission tomography. IEEE Transactions on Medical Imaging. 2006;25 (8) :1068-1078.

2005

  • Jeong J-W, Kim T-S, Shin DC, Do S, Singh M, Marmarelis VZ. Soft tissue differentiation using multiband signatures of high resolution ultrasonic transmission tomography. IEEE Transactions on Medical Imaging. 2005;24 (3) :399-408.

2004

  • Jeong J-W, Kim T-S, Shin DC, Do S. Multi-band Tissue Classification for Ultrasonic Transmission Tomography Using Spectral Profile Detection, in SPIE. Vol 5373. Medical Imaging 2004: Ultrasonic Imaging and Signal Processing. San Diego, CA ; 2004.