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Artificial Intelligence Resource (AIR)

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About AIR

A New CCR Initiative for Researchers

Artificial Intelligence Resource (AIR) is a collaborative research group focusing on developing translational computer vision-based AI models for cancer research in the CCR.

Mission:

The goal of Artificial Intelligence Resource (AIR) is to make AI tools available to Center for Cancer Research (CCR) investigators. The strength of AI is that algorithms can be trained to seek specific information that may be scientifically or clinically important.  AIR will mainly focus on “Computer Vision” which analyzes medical images, such as radiologic, digital pathology, video/endoscopy, and optical imaging among others.  Examples of potential projects include developing better screening, detection methods or predictive markers, or improving procedures among many others. Both clinical and laboratory-based imaging projects will be considered. Please refer to our ongoing Projects and Prior Publications for more information.

To collaborate with the AIR:

Please click the button below and fill out the form.  Once collaboration form is completed click “Submit”.

Contact Information

Artificial Intelligence Resource (AIR)

Center for Cancer Research

National Cancer Institute
5413 W. Cedar Lane, Suite 102-C
Bethesda, MD 20814
240-585-3000
 
 

For Additional Information please visit: Molecular Imaging Branch

 

Projects

Projects

Multiresolution Application of Artificial Intelligence in Digital Pathology for Prediction of Positive Lymph Nodes From Primary Tumors in Bladder Cancer.

Objective: to develop an artificial intelligence (AI)-based model for identifying patients with lymph node (LN) metastasis based on digital evaluation of primary tumors. Link to publication: PMID 32330067

Detection of prostate cancer in multiparametric MRI using random forest with instance weighting.

Objective: prostate computer-aided diagnosis (CAD) based on random forest to detect prostate cancer using a combination of spatial, intensity, and texture features extracted from three sequences, T2W, ADC, and B2000 images. Link to publication: PMID: 28630883

Research Staff & Steering Committee

Research Staff

 
 

Baris Turkbey, M.D.

Section Head

Dr. Turkbey obtained his medical degree from Hacettepe University in Ankara, Turkey in 2003. He completed his residency in Diagnostic and Interventional Radiology at Hacettepe University. He joined Molecular Imaging Branch (MIB), National Cancer Institute, NIH in 2007. His main research areas are imaging of prostate cancer (multiparametric MRI, PET CT), image guided biopsy and treatment techniques (focal therapy, surgery, and radiation therapy) for prostate cancer and artificial intelligence. Dr. Turkbey is a member of Prostate Imaging Reporting & Data System (PI-RADS) Steering Committee. He is the Head of the Magnetic Resonance Imaging section in MIB and the Artificial Intelligence Resource in MIB.

BG 10 Room B3B85
240-760-6112
turkbeyi@mail.nih.gov

G. Thomas Brown, M.D., Ph.D.

Staff Clinician

G. Thomas Brown, MD, PhD received his PhD in Cell Biology and MD from Case Western Reserve University in 2013 received training in Anatomic Pathology in the NCI Laboratory of Pathology. He completed a Clinical Informatics Research Fellowship at the National Library of Medicine. He joined the Leidos Biomedical ABCS/IVG as a bioinformatics analyst in 2019 before transitioning to Assistant Research Physician with NCI in 2020. His areas of interest involve computer vision and deep-learning algorithm development to assist physicians with diagnosing and treating cancers with greater accuracy and efficacy. List of publications:

BG 10 Room B3B85
301-846-1847
301-263-4048
gtom.brown@nih.gov

 

Nathan Lay, Ph.D.

Staff Scientist 

Dr. Lay received his PhD in Computational Science from Florida State University in 2013 where he developed a novel machine learning aggregation framework based on ideas from prediction markets. He spent three years in industry research where he developed methods for segmentation and landmark localization in medical image analysis problems. As a result of his work in the industry, he is a co-inventor of several patents. After three years of industry research, he joined the Imaging Biomarkers and Computer-Aided Diagnosis Laboratory at the National Institutes of Health as a Staff Scientist where he developed novel prostate cancer detection methods. Through close collaboration with the Molecular Imaging Program, his prostate cancer detection systems have been honed and studied in three international reader studies. His research interests are in the fields of machine learning and computer vision.

BG 10 RM B3B69
240-858-7063
301-768-5257
nathan.lay@nih.gov

Sushant Patkar, Ph.D.

Post-doc Fellow

Dr. Patkar received his PhD in Computer Science in May 2021 from the University of Maryland, College Park, under the mentorship of Dr. Eytan Ruppin. In June 2021, he started his postdoc at the Artificial Intelligence Resource, Molecular Imaging Branch, NCI, where he began developing advanced deep learning-based algorithms to automatically extract and quantify prognostic features from gigapixel-sized digital pathology images of cancer patients. He has a strong background in computer science, machine learning, and with expertise in the development of computational deconvolution methods that can analyze bulk DNA and RNA Sequencing data to characterize the tumor microenvironment and predict responses to immune checkpoint blockade therapies. His current research focuses on the development of Artificial Intelligence (AI) approaches for spatial analysis of the tumor microenvironment to identify novel biomarkers predictive of response to immune checkpoint blockade therapies. (Google scholar profile: https://scholar.google.com/citations?user=AsIX1JAAAAAJ&hl=en )

BG 10 RM B3B69
240-858-3172
patkar.sushant@nih.gov

Fahmida Haque, Ph.D.

Postdoc Fellow

Dr. Fahmida Haque works as a postdoctoral fellow at Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, USA. She has obtained her doctoral degree in Electrical, Electronic and System Engineering, from National University of Malaysia, Malaysia in 2022. She completed her undergraduate program in electrical and electronic engineering from American International University-Bangladesh in 2016. Her main research areas are machine learning for biomedical application, carcer detection from radiological and pathological images, and artificial intelligent based diagnosis systems, computational neuroscience.

BG 10 RM B3B69
240-620-0811
fahmida.haque@nih.gov

Kutsev Ozyoruk, Ph.D.

Postdoc Fellow

Dr. Kutsev B. Ozyoruk received her BSc in mathematics from Bosphorus University in 2014, followed by an MSc. After two years of industry experience as a data scientist at AVL Research and Engineering company, she completed her PhD in biomedical engineering in 2021 at Bosphorus University, where she researched deep learning approaches for localizing capsule endoscopes. She pursued postdoctoral research in computational pathology at Harvard Medical School upon completion of her PhD before joining Dr. Baris Turkbey’s group within the Molecular Imaging Branch, National Cancer Institute in 2023. Throughout her research journey, she developed a novel generative artificial intelligence approach for frozen section diagnostic quality improvement, a novel depth estimation method from monocular video, and decision tree-based motion estimation method which is validated in real life traffic scenarios. Dr. Ozyoruk’s current research focus, as a postdoctoral researcher, revolves around developing AI-assisted diagnostic tools for radiology, computational pathology, and endoscopy.

BG 10 RM B3B69
240-858-7063
kutsev.ozyoruk@nih.gov

Alex Chen, BS, MS

PostBac Fellow

Alex Chen joined the Molecular Imaging Branch as an NIH postbaccalaureate fellow after completing his Bachelor of Arts in Biochemistry and Master of Science in Chemistry at the University of Pennsylvania in 2023. His primary research interests currently revolve around the segmentation of prostate cancer and thymoma. Specifically, his work involves investigating the potential integration of radiology reports to enhance existing AI algorithms for detecting prostate cancer. In addition, he is exploring the use of AI to decipher spatial patterns within the tumor immune microenvironment. Alex is actively pursuing a dual MD/PhD degree, with a vision of becoming a physician-scientist in the future. In his free time, he enjoys playing computer games, playing squash, and taking his dog on hikes through the local trails.

BG 10 RM B3B69
301-858-3332
alex.chen3@nih.gov

Affiliated Researchers

Sophia Ty, BS

Benjamin Simon, BS

Zhijun Chen, Ph.D.

Harry Zhang, Ph.D.

Omer Tarik Esengur, M.D.

David Gelikman, BA

Enis C. Yilmaz, M.D.

Stephanie A. Harmon, Ph.D.

 
 

Steering Committee

Section Head

Ismail Baris Turkbey
 
 
 
Senior Clinician
National Cancer Institute, National Institutes of Health 
Building 10 – Room B3B85
240-760-6112
turkbeyi@mail.nih.gov
 
 
 

Steering Committee Members

 
 
Director, Molecular Imaging Program
National Cancer Institute, National Institutes of Health
 
 
National Library of Medicine, National Institutes of Health
 
 
 
 
 
Center for Research in Computer Vision, University of Central Florida
 
 
 
 
National Cancer Institute, National Institutes of Health
 
 
 
 
National Cancer Institute, National Institutes of Health
 
 
 
 

Alumni & Available Positions

Usamah Chaudhary

Alena Arlova

Kevin Ma

Latrice Johnson

Anastasia Duenas

Md Abdul Kader Sagar

Tim Phelps

Nicole Tran

Katie Merriman

No positions available at this time.

Publications & Events

  • Johnson LA, Harmon SA, Yilmaz EC, Lin Y, Belue MJ, Merriman KM, Lay NS, Sanford TH, Sarma KV, Arnold CW, Xu Z, Roth HR, Yang D, Tetreault J, Xu D, Patel KR, Gurram S, Wood BJ, Citrin DE, Pinto PA, Choyke PL, Turkbey B. Automated prostate gland segmentation in challenging clinical cases: comparison of three artificial intelligence methods. Abdom Radiol (NY). 2024 Mar 21. doi: 10.1007/s00261-024-04242-7. Epub ahead of print. PMID: 38512516.
  • Anari PY, Lay N, Zahergivar A, Firouzabadi FD, Chaurasia A, Golagha M, Singh S, Homayounieh F, Obiezu F, Harmon S, Turkbey E, Merino M, Jones EC, Ball MW, Linehan WM, Turkbey B, Malayeri AA. Deep learning algorithm (YOLOv7) for automated renal mass detection on contrast-enhanced MRI: a 2D and 2.5D evaluation of results. Abdom Radiol (NY). 2024 Apr;49(4):1194-1201. doi: 10.1007/s00261-023-04172-w. Epub 2024 Feb 17. PMID: 38368481.
  • Yazdian Anari P, Zahergivar A, Gopal N, Chaurasia A, Lay N, Ball MW, Turkbey B, Turkbey E, Jones EC, Linehan WM, Malayeri AA. Kidney scoring surveillance: predictive machine learning models for clear cell renal cell carcinoma growth using MRI. Abdom Radiol (NY). 2024 Apr;49(4):1202-1209. doi: 10.1007/s00261-023-04162-y. Epub 2024 Feb 12. PMID: 38347265.
  • Zahergivar A, Yazdian Anari P, Mendhiratta N, Lay N, Singh S, Dehghani Firouzabadi F, Chaurasia A, Golagha M, Homayounieh F, Gautam R, Harmon S, Turkbey E, Merino M, Jones EC, Ball MW, Turkbey B, Linehan WM, Malayeri AA. Non-Invasive Tumor Grade Evaluation in Von Hippel-Lindau-Associated Clear Cell Renal Cell Carcinoma: A Magnetic Resonance Imaging-Based Study. J Magn Reson Imaging. 2024 Feb 1. doi: 10.1002/jmri.29222. Epub ahead of print. PMID: 38299714.
  • Belue MJ, Harmon SA, Yang D, An JY, Gaur S, Law YM, Turkbey E, Xu Z, Tetreault J, Lay NS, Yilmaz EC, Phelps TE, Simon B, Lindenberg L, Mena E, Pinto PA, Bagci U, Wood BJ, Citrin DE, Dahut WL, Madan RA, Gulley JL, Xu D, Choyke PL, Turkbey B. Deep Learning-Based Detection and Classification of Bone Lesions on Staging Computed Tomography in Prostate Cancer: A Development Study. Acad Radiol. 2024 Jan 22:S1076-6332(24)00008-4. doi: 10.1016/j.acra.2024.01.009. Epub ahead of print. PMID: 38262813.
  • Kaczanowska S, Murty T, Alimadadi A, Contreras CF, Duault C, Subrahmanyam PB, Reynolds W, Gutierrez NA, Baskar R, Wu CJ, Michor F, Altreuter J, Liu Y, Jhaveri A, Duong V, Anbunathan H, Ong C, Zhang H, Moravec R, Yu J, Biswas R, Van Nostrand S, Lindsay J, Pichavant M, Sotillo E, Bernstein D, Carbonell A, Derdak J, Klicka-Skeels J, Segal JE, Dombi E, Harmon SA, Turkbey B, Sahaf B, Bendall S, Maecker H, Highfill SL, Stroncek D, Glod J, Merchant M, Hedrick CC, Mackall CL, Ramakrishna S, Kaplan RN. Immune determinants of CAR-T cell expansion in solid tumor patients receiving GD2 CAR-T cell therapy. Cancer Cell. 2024 Jan 8;42(1):35-51.e8. doi: 10.1016/j.ccell.2023.11.011. Epub 2023 Dec 21. PMID: 38134936; PMCID: PMC10947809.
  • Belue MJ, Harmon SA, Masoudi S, Barrett T, Law YM, Purysko AS, Panebianco V, Yilmaz EC, Lin Y, Jadda PK, Raavi S, Wood BJ, Pinto PA, Choyke PL, Turkbey B. Quality of T2-weighted MRI re-acquisition versus deep learning GAN image reconstruction: A multi-reader study. Eur J Radiol. 2024 Jan;170:111259. doi: 10.1016/j.ejrad.2023.111259. Epub 2023 Dec 12. PMID: 38128256; PMCID: PMC10842312.
  • Gelikman DG, Rais-Bahrami S, Pinto PA, Turkbey B. AI-powered radiomics: revolutionizing detection of urologic malignancies. Curr Opin Urol. 2024 Jan 1;34(1):1-7. doi: 10.1097/MOU.0000000000001144. Epub 2023 Nov 1. PMID: 37909882; PMCID: PMC10842165.
  • Lin Y, Belue MJ, Yilmaz EC, Harmon SA, An J, Law YM, Hazen L, Garcia C, Merriman KM, Phelps TE, Lay NS, Toubaji A, Merino MJ, Wood BJ, Gurram S, Choyke PL, Pinto PA, Turkbey B. Deep Learning-Based T2-weighted MR Image Quality Assessment and Its Impact on Prostate Cancer Detection Rates. J Magn Reson Imaging. 2023 Oct 9. doi: 10.1002/jmri.29031. Epub ahead of print. PMID: 37811666.
  • Patkar S, Mannheimer J, Harmon S, Mazcko C, Choyke P, Brown GT, Turkbey B, LeBlanc A, Beck J. Large Scale Comparative Deconvolution Analysis of the Canine and Human Osteosarcoma Tumor Microenvironment Uncovers Conserved Clinically Relevant Subtypes. bioRxiv [Preprint]. 2023 Sep 29:2023.09.27.559797. doi: 10.1101/2023.09.27.559797. PMID: 37808704; PMCID: PMC10557692.
  • Yilmaz EC, Harmon SA, Belue MJ, Merriman KM, Phelps TE, Lin Y, Garcia C, Hazen L, Patel KR, Merino MJ, Wood BJ, Choyke PL, Pinto PA, Citrin DE, Turkbey B. Evaluation of a Deep Learning-based Algorithm for Post-Radiotherapy Prostate Cancer Local Recurrence Detection Using Biparametric MRI. Eur J Radiol. 2023 Nov;168:111095. doi: 10.1016/j.ejrad.2023.111095. Epub 2023 Sep 13. PMID: 37717420; PMCID: PMC10615746.
  • Patel P, Harmon S, Iseman R, Ludkowski O, Auman H, Hawley S, Newcomb LF, Lin DW, Nelson PS, Feng Z, Boyer HD, Tretiakova MS, True LD, Vakar-Lopez F, Carroll PR, Cooperberg MR, Chan E, Simko J, Fazli L, Gleave M, Hurtado-Coll A, Thompson IM, Troyer D, McKenney JK, Wei W, Choyke PL, Bratslavsky G, Turkbey B, Siemens DR, Squire J, Peng YP, Brooks JD, Jamaspishvili T. Artificial Intelligence-Based PTEN Loss Assessment as an Early Predictor of Prostate Cancer Metastasis After Surgery: A Multicenter Retrospective Study. Mod Pathol. 2023 Oct;36(10):100241. doi: 10.1016/j.modpat.2023.100241. Epub 2023 Jun 19. PMID: 37343766; PMCID: PMC10592257.
  • Lay N, Anari PY, Chaurasia A, Firouzabadi FD, Harmon S, Turkbey E, Gautam R, Samimi S, Merino MJ, Ball MW, Linehan WM, Turkbey B, Malayeri AA. Deep learning-based decision forest for hereditary clear cell renal cell carcinoma segmentation on MRI. Med Phys. 2023 Aug;50(8):5020-5029. doi: 10.1002/mp.16303. Epub 2023 Mar 13. PMID: 36855860; PMCID: PMC10683486.
  • Yilmaz EC, Belue MJ, Turkbey B, Reinhold C, Choyke PL. A Brief Review of Artificial Intelligence in Genitourinary Oncological Imaging. Can Assoc Radiol J. 2023 Aug;74(3):534-547. doi: 10.1177/08465371221135782. Epub 2022 Dec 14. PMID: 36515576.
  • Patkar S, Beck J, Harmon S, Mazcko C, Turkbey B, Choyke P, Brown GT, LeBlanc A. Deep Domain Adversarial Learning for Species-Agnostic Classification of Histologic Subtypes of Osteosarcoma. Am J Pathol. 2023 Jan;193(1):60-72. doi: 10.1016/j.ajpath.2022.09.009. Epub 2022 Oct 27. PMID: 36309101; PMCID: PMC9798510.
  • U Chaudhary, PA Desai, N Takahashi, N Lay, PL Choyke, A Thomas. Automated detection and segmentation of small cell lung cancer liver metastases on CT. Journal of Clinical Oncology 40 (16_suppl), e13555-e13555, 2022.
  • KC Ma, MV Green, EM Jagoda, J Seidel, PL Choyke, B Turkbey, Harmon S. Deep learning vs. conventional methods for automatic quantification of total tumor radioactivity in positron projection images of mouse xenograft tumors. Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging, 2022.
  • Anari PY, Lay N, Gopal N, Chaurasia A, Samimi S, Harmon S, Firouzabadi FD, Merino MJ, Wakim P, Turkbey E, Jones EC, Ball MW, Turkbey B, Linehan WM, Malayeri AA. An MRI-based radiomics model to predict clear cell renal cell carcinoma growth rate classes in patients with von Hippel-Lindau syndrome. Abdom Radiol (NY). 2022 Oct;47(10):3554-3562. doi: 10.1007/s00261-022-03610-5. Epub 2022 Jul 22. PMID: 35869307; PMCID: PMC10645140.
  • Arlova A, Jin C, Wong-Rolle A, Chen ES, Lisle C, Brown GT, Lay N, Choyke PL, Turkbey B, Harmon S, Zhao C. Artificial Intelligence-based Tumor Segmentation in Mouse Models of Lung Adenocarcinoma. J Pathol Inform. 2022 Jan 20;13:100007. doi: 10.1016/j.jpi.2022.100007. PMID: 35242446; PMCID: PMC8860735.
  • Belue MJ, Harmon SA, Patel K, Daryanani A, Yilmaz EC, Pinto PA, Wood BJ, Citrin DE, Choyke PL, Turkbey B. Development of a 3D CNN-based AI Model for Automated Segmentation of the Prostatic Urethra. Acad Radiol. 2022 Sep;29(9):1404-1412. doi: 10.1016/j.acra.2022.01.009. Epub 2022 Feb 16. PMID: 35183438; PMCID: PMC9339453.
  • Mehralivand S, Yang D, Harmon SA, Xu D, Xu Z, Roth H, Masoudi S, Kesani D, Lay N, Merino MJ, Wood BJ, Pinto PA, Choyke PL, Turkbey B. Deep learning-based artificial intelligence for prostate cancer detection at biparametric MRI. Abdom Radiol (NY). 2022 Apr;47(4):1425-1434. doi: 10.1007/s00261-022-03419-2. Epub 2022 Jan 31. PMID: 35099572; PMCID: PMC10506420.
  • Mehralivand S, Yang D, Harmon SA, Xu D, Xu Z, Roth H, Masoudi S, Sanford TH, Kesani D, Lay NS, Merino MJ, Wood BJ, Pinto PA, Choyke PL, Turkbey B. A Cascaded Deep Learning-Based Artificial Intelligence Algorithm for Automated Lesion Detection and Classification on Biparametric Prostate Magnetic Resonance Imaging. Acad Radiol. 2022 Aug;29(8):1159-1168. doi: 10.1016/j.acra.2021.08.019. Epub 2021 Sep 28. PMID: 34598869; PMCID: PMC10575564.
  • Dayan I, Roth HR, Zhong A, Harouni A, Gentili A, Abidin AZ, Liu A, Costa AB, Wood BJ, Tsai CS, Wang CH, Hsu CN, Lee CK, Ruan P, Xu D, Wu D, Huang E, Kitamura FC, Lacey G, de Antônio Corradi GC, Nino G, Shin HH, Obinata H, Ren H, Crane JC, Tetreault J, Guan J, Garrett JW, Kaggie JD, Park JG, Dreyer K, Juluru K, Kersten K, Rockenbach MABC, Linguraru MG, Haider MA, AbdelMaseeh M, Rieke N, Damasceno PF, E Silva PMC, Wang P, Xu S, Kawano S, Sriswasdi S, Park SY, Grist TM, Buch V, Jantarabenjakul W, Wang W, Tak WY, Li X, Lin X, Kwon YJ, Quraini A, Feng A, Priest AN, Turkbey B, Glicksberg B, Bizzo B, Kim BS, Tor-Díez C, Lee CC, Hsu CJ, Lin C, Lai CL, Hess CP, Compas C, Bhatia D, Oermann EK, Leibovitz E, Sasaki H, Mori H, Yang I, Sohn JH, Murthy KNK, Fu LC, de Mendonça MRF, Fralick M, Kang MK, Adil M, Gangai N, Vateekul P, Elnajjar P, Hickman S, Majumdar S, McLeod SL, Reed S, Gräf S, Harmon S, Kodama T, Puthanakit T, Mazzulli T, de Lavor VL, Rakvongthai Y, Lee YR, Wen Y, Gilbert FJ, Flores MG, Li Q. Federated learning for predicting clinical outcomes in patients with COVID-19. Nat Med. 2021 Oct;27(10):1735-1743. doi: 10.1038/s41591-021-01506-3. Epub 2021 Sep 15. PMID: 34526699; PMCID: PMC9157510.
  • Sanford TH, Zhang L, Harmon SA, Sackett J, Yang D, Roth H, Xu Z, Kesani D, Mehralivand S, Baroni RH, Barrett T, Girometti R, Oto A, Purysko AS, Xu S, Pinto PA, Xu D, Wood BJ, Choyke PL, Turkbey B. Data Augmentation and Transfer Learning to Improve Generalizability of an Automated Prostate Segmentation Model. AJR Am J Roentgenol. 2020 Dec;215(6):1403-1410. doi: 10.2214/AJR.19.22347. Epub 2020 Oct 14. PMID: 33052737; PMCID: PMC8974988.
  • Harmon SA, Patel PG, Sanford TH, Caven I, Iseman R, Vidotto T, Picanço C, Squire JA, Masoudi S, Mehralivand S, Choyke PL, Berman DM, Turkbey B, Jamaspishvili T. High throughput assessment of biomarkers in tissue microarrays using artificial intelligence: PTEN loss as a proof-of-principle in multi-center prostate cancer cohorts. Mod Pathol. 2021 Feb;34(2):478-489. doi: 10.1038/s41379-020-00674-w. Epub 2020 Sep 3. PMID: 32884130; PMCID: PMC9152638.
  • Harmon SA, Sanford TH, Xu S, Turkbey EB, Roth H, Xu Z, Yang D, Myronenko A, Anderson V, Amalou A, Blain M, Kassin M, Long D, Varble N, Walker SM, Bagci U, Ierardi AM, Stellato E, Plensich GG, Franceschelli G, Girlando C, Irmici G, Labella D, Hammoud D, Malayeri A, Jones E, Summers RM, Choyke PL, Xu D, Flores M, Tamura K, Obinata H, Mori H, Patella F, Cariati M, Carrafiello G, An P, Wood BJ, Turkbey B. Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets. Nat Commun. 2020 Aug 14;11(1):4080. doi: 10.1038/s41467-020-17971-2. PMID: 32796848; PMCID: PMC7429815.
  • Sanford T, Harmon SA, Turkbey EB, Kesani D, Tuncer S, Madariaga M, Yang C, Sackett J, Mehralivand S, Yan P, Xu S, Wood BJ, Merino MJ, Pinto PA, Choyke PL, Turkbey B. Deep-Learning-Based Artificial Intelligence for PI-RADS Classification to Assist Multiparametric Prostate MRI Interpretation: A Development Study. J Magn Reson Imaging. 2020 Nov;52(5):1499-1507. doi: 10.1002/jmri.27204. Epub 2020 Jun 1. PMID: 32478955; PMCID: PMC8942293.
  • Harmon SA, Sanford TH, Brown GT, Yang C, Mehralivand S, Jacob JM, Valera VA, Shih JH, Agarwal PK, Choyke PL, Turkbey B. Multiresolution Application of Artificial Intelligence in Digital Pathology for Prediction of Positive Lymph Nodes From Primary Tumors in Bladder Cancer. JCO Clin Cancer Inform. 2020 Apr;4:367-382. doi: 10.1200/CCI.19.00155. PMID: 32330067; PMCID: PMC7259877.