Publications.

Last updated October, 2024

Find below a list of MIDRC peer-reviewed publications and preprints, plus a selection of other publications, grants, and presentations.

Peer-reviewed literature.

  1. Van Veen, Dave, Cara Van Uden, Louis Blankemeier, Jean-Benoit Delbrouck, Asad Aali, Christian Bluethgen, Anuj Pareek et al. "Adapted large language models can outperform medical experts in clinical text summarization." Nature medicine 30, no. 4 (2024): 1134-1142.

  2. Yang, Yuzhe, Haoran Zhang, Judy W. Gichoya, Dina Katabi, and Marzyeh Ghassemi. "The limits of fair medical imaging AI in real-world generalization." Nature Medicine (2024): 1-11.

  3. Kandarpa, Kris. "Regulation of AI algorithms for clinical decision support: a personal opinion." International Journal of Computer Assisted Radiology and Surgery (2024): 1-3.

  4. Drukker, Karen, Berkman Sahiner, Tingting Hu, Grace Hyun Kim, Heather M. Whitney, Natalie Baughan, Kyle J. Myers, Maryellen L. Giger, Michael McNitt-Gray, MIDRC-MetricTree: a decision tree-based tool for recommending performance metrics in artificial intelligence-assisted medical image analysis, J. Med. Imag. 11(2), 024504 (2024), doi: 10.1117/1.JMI.11.2.024504.

  5. Gorre, Naveena, Eduardo Carranza, Jordan Fuhrman, Hui Li, Ravi K. Madduri, Maryellen Giger, and Issam El Naqa. "MIDRC CRP10 AI interface—an integrated tool for exploring, testing and visualization of AI models." Physics in Medicine & Biology 68, no. 7 (2023): 074002.

  6. Pyrros, A., Chen, A., Rodríguez-Fernández, J.M., Borstelmann, S.M., Cole, P.A., Horowitz, J., Chung, J., Nikolaidis, P., Boddipalli, V., Siddiqui, N. and Willis, M., 2023. Deep Learning–Based Digitally Reconstructed Tomography of the Chest in the Evaluation of Solitary Pulmonary Nodules: A Feasibility Study. Academic radiology, 30(4), pp.739-748.

  7. Li, Hui, Karen Drukker, Qiyuan Hu, Heather M. Whitney, Jordan D. Fuhrman, and Maryellen L. Giger. "Predicting intensive care need for COVID-19 patients using deep learning on chest radiography." Journal of Medical Imaging 10, no. 4 (2023): 044504.

  8. Shenouda, Mena, Isabella Flerlage, Aditi Kaveti, Maryellen L. Giger, and Samuel G. Armato III. "Assessment of a deep learning model for COVID-19 classification on chest radiographs: a comparison across image acquisition techniques and clinical factors." Journal of Medical Imaging 10, no. 6 (2023): 064504-064504.

  9. Baughan, Natalie, Heather M. Whitney, Karen Drukker, Berkman Sahiner, Tingting Hu, Grace Hyun Kim, Michael McNitt-Gray, Kyle J. Myers, and Maryellen L. Giger. "Sequestration of imaging studies in MIDRC: stratified sampling to balance demographic characteristics of patients in a multi-institutional data commons." Journal of Medical Imaging 10, no. 6 (2023): 064501-064501.

  10. Armato, Samuel, Karen Drukker, and Lubomir Hadjiiski. "AI in medical imaging grand challenges: translation from competition to research benefit and patient care." The British Journal of Radiology (2023): 20221152. https://doi.org/10.1259/bjr.20221152

  11. Guo, Xiaoyuan, Judy Wawira Gichoya, Hari Trivedi, Saptarshi Purkayastha, and Imon Banerjee. "MedShift: Automated Identification of Shift Data for Medical Image Dataset Curation." IEEE Journal of Biomedical and Health Informatics (2023).

  12. Zhang R, Griner D, Garrett JW, Qi Z, Chen GH. Training certified detectives to track down the intrinsic shortcuts in COVID-19 chest x-ray data sets. Sci Rep. 2023 Aug 4;13(1):12690. doi: 10.1038/s41598-023-39855-3. PMID: 37542078; PMCID: PMC10403557.

  13. Chen, W., Sá, R.C., Bai, Y., Napel, S., Gevaert, O., Lauderdale, D.S. and Giger, M.L., 2023. Machine learning with multimodal data for COVID-19. Heliyon, vol. 9, no. 7, 2023, https://doi.org/10.1016/j.heliyon.2023.e17934. 

  14. Heather M. Whitney, Natalie Baughan, Kyle J. Myers, Karen Drukker, Judy Gichoya, Brad Bower, Weijie Chen, Nicholas Gruszauskas, Jayashree Kalpathy-Cramer, Sanmi Koyejo, Berkman Sahiner, Zi Zhang, Maryellen L. Giger, Longitudinal assessment of demographic representativeness in the Medical Imaging and Data Resource Center open data commons, J. Med. Imag. 10(6), 061105 (2023), doi: 10.1117/1.JMI.10.6.061105.

  15. Banerjee, Imon, Kamanasish Bhattacharjee, John L. Burns, Hari Trivedi, Saptarshi Purkayastha, Laleh Seyyed-Kalantari, Bhavik N. Patel, Rakesh Shiradkar, and Judy Gichoya. "“Shortcuts” causing bias in radiology artificial intelligence: causes, evaluation and mitigation." Journal of the American College of Radiology (2023), https://doi.org/10.1016/j.jacr.2023.06.025

  16. Ardestani, Ali, et al. “External Covid-19 Deep Learning Model Validation on ACR AI-Lab: It’s a Brave New World.” Journal of the American College of Radiology, vol. 19, no. 7, 2022, pp. 891–900, https://doi.org/10.1016/j.jacr.2022.03.013. 

  17. Peng L, Luo G, Walker A, Zaiman Z, Jones EK, Gupta H, Kersten K, Burns JL, Harle CA, Magoc T, Shickel B, Steenburg SD, Loftus T, Melton GB, Gichoya JW, Sun J, Tignanelli CJ. ‘Evaluation of federated learning variations for COVID-19 diagnosis using chest radiographs from 42 US and European hospitals.’ J Am Med Inform Assoc. 2022 Dec 13;30(1):54-63. doi: 10.1093/jamia/ocac188. PMID: 36214629; PMCID: PMC9619688.

  18. Jean-Benoit Delbrouck, Pierre Chambon, Christian Bluethgen, Emily Tsai, Omar Almusa, and Curtis Langlotz. 2022. ‘Improving the Factual Correctness of Radiology Report Generation with Semantic Rewards.’ In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 4348–4360, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.

  19. Chambon PJ, Wu C, Steinkamp JM, Adleberg J, Cook TS, Langlotz CP. ‘Automated deidentification of radiology reports combining transformer and "hide in plain sight" rule-based methods.’ J Am Med Inform Assoc. 2023 Jan 18;30(2):318-328. doi: 10.1093/jamia/ocac219. PMID: 36416419; PMCID: PMC9846681.

  20. Drukker K., Chen W., Gichoya J., Gruszauskas N., Kalpathy-Cramer J., Koyejo S., Myers K., Sá R.C., Sahiner B., Whitney H., Zhang Z., Giger M.L.,’Toward fairness in artificial intelligence for medical image analysis: identification and mitigation of potential biases in the roadmap from data collection to model deployment.’, J. Med. Imag. 10(6), 061104 (2023), doi: 10.1117/1.JMI.10.6.061104.

  21. Tariq, Amara, Siyi Tang, Hifza Sakhi, Leo Anthony Celi, Janice M. Newsome, Daniel L. Rubin, Hari Trivedi, Judy Wawira Gichoya, and Imon Banerjee. "Fusion of imaging and non-imaging data for disease trajectory prediction for coronavirus disease 2019 patients." Journal of Medical Imaging 10, no. 3 (2023): 034004-034004.

  22. Chambon, P., Cook, T.S. & Langlotz, C.P., Improved Fine-Tuning of In-Domain Transformer Model for Inferring COVID-19 Presence in Multi-Institutional Radiology Reports. J Digit Imaging (2022). https://doi.org/10.1007/s10278-022-00714-8

  23. Chambon, P.J., Wu, C., Steinkamp, J.M., Adleberg, J., Cook, T.S. and Langlotz, C.P., 2022. Automated deidentification of radiology reports combining transformer and “hide in plain sight” rule-based methods. Journal of the American Medical Informatics Association.

  24. Pyrros A, Rodriguez Fernandez J, Borstelmann SM, Flanders A, Wenzke D, et al. Validation of a deep learning, value-based care model to predict mortality and comorbidities from chest radiographs in COVID-19. PLOS Digital Health, 1(8): e0000057, (2022), https://doi.org/10.1371/journal.pdig.0000057

  25. Gichoya JW, Banerjee I, Bhimireddy AR, Burns JL, Celi LA, Chen LC, Correa R, Dullerud N, Ghassemi M, Huang SC, Kuo PC, Lungren MP, Palmer LJ, Price BJ, Purkayastha S, Pyrros AT, Oakden-Rayner L, Okechukwu C, Seyyed-Kalantari L, Trivedi H, Wang R, Zaiman Z, Zhang H. AI recognition of patient race in medical imaging: a modelling study. Lancet Digit Health. 2022 Jun;4(6):e406-e414. doi: 10.1016/S2589-7500(22)00063-2. Epub 2022 May 11. PMID: 35568690.

  26. Sun J, Peng L, Li T, Adila D, Zaiman Z, Melton-Meaux GB, Ingraham NE, Murray E, Boley D, Switzer S, Burns JL, Huang K, Allen T, Steenburg SD, Gichoya JW, Kummerfeld E, Tignanelli CJ. Performance of a Chest Radiograph AI Diagnostic Tool for COVID-19: A Prospective Observational Study. Radiol Artif Intell. 2022 Jun 1;4(4):e210217. doi: 10.1148/ryai.210217. PMID: 35923381; PMCID: PMC9344211.

  27. Nguyen XV, Dikici E, Candemir S, Ball RL, Prevedello LM. Mortality Prediction Analysis among COVID-19 Inpatients Using Clinical Variables and Deep Learning Chest Radiography Imaging Features. Tomography. 2022 Jul 13;8(4):1791-1803. doi: 10.3390/tomography8040151. PMID: 35894016; PMCID: PMC9326627.

  28. Ardestani A, Li MD, Chea P, Wortman JR, Medina A, Kalpathy-Cramer J, Wald C. External COVID-19 Deep Learning Model Validation on ACR AI-LAB: It's a Brave New World. J Am Coll Radiol. 2022 Jul;19(7):891-900. doi: 10.1016/j.jacr.2022.03.013. Epub 2022 Apr 8. PMID: 35483438; PMCID: PMC8989698.

  29. Patel NJ, D'Silva KM, Li MD, Hsu TY, DiIorio M, Fu X, Cook C, Prisco L, Martin L, Vanni KMM, Zaccardelli A, Zhang Y, Kalpathy-Cramer J, Sparks JA, Wallace ZS. Assessing the Severity of COVID-19 Lung Injury in Rheumatic Diseases versus the General Population Using Deep Learning-Derived Chest Radiograph Scores. Arthritis Care Res (Hoboken). 2022 Mar 21:10.1002/acr.24883. doi: 10.1002/acr.24883. Epub ahead of print. PMID: 35313091; PMCID: PMC9081965.

  30. Li MD, Arun NT, Aggarwal M, Gupta S, Singh P, Little BP, Mendoza DP, Corradi GCA, Takahashi MS, Ferraciolli SF, Succi MD, Lang M, Bizzo BC, Dayan I, Kitamura FC, Kalpathy-Cramer J. Multi-population generalizability of a deep learning-based chest radiograph severity score for COVID-19. Medicine (Baltimore). 2022 Jul 22;101(29):e29587. doi: 10.1097/MD.0000000000029587. PMID: 35866818; PMCID: PMC9302282.

  31. Gichoya JW, Sinha P, Davis M, Dunkle JW, Hamlin SA, Herr KD, Hoff CN, Letter HP, McAdams CR, Puthoff GD, Smith KL. Multireader evaluation of radiologist performance for COVID-19 detection on emergency department chest radiographs. Clinical Imaging. 2022 Feb 1;82:77-82.

  32. Pyrros A, Rodríguez-Fernández JM, Borstelmann SM, Gichoya JW, Horowitz JM, Fornelli B, Siddiqui N, Velichko Y, Sanmi OK, Galanter W. Detecting Racial/Ethnic Health Disparities Using Deep Learning From Frontal Chest Radiography. Journal of the American College of Radiology. 2022 Jan 1;19(1):184-91.

  33. Gibson LE, Fenza RD, Lang M, Capriles MI, Li MD, Kalpathy-Cramer J, Little BP, Arora P, Mueller AL, Ichinose F, Bittner EA, Berra L, G Chang M. Right Ventricular Strain Is Common in Intubated COVID-19 Patients and Does Not Reflect Severity of Respiratory Illness. J Intensive Care Med. 2021 Aug;36(8):900-909. doi: 10.1177/08850666211006335. Epub 2021 Mar 30. PMID: 33783269; PMCID: PMC8267080.

  34. Li MD, Little BP, Alkasab TK, Mendoza DP, Succi MD, Shepard JO, Lev MH, Kalpathy-Cramer J. Multi-Radiologist User Study for Artificial Intelligence-Guided Grading of COVID-19 Lung Disease Severity on Chest Radiographs. Acad Radiol. 2021 Apr;28(4):572-576. doi: 10.1016/j.acra.2021.01.016. Epub 2021 Jan 18. PMID: 33485773; PMCID: PMC7813473.

  35. Lang M, Li MD, Jiang KZ, Yoon BC, Mendoza DP, Flores EJ, Rincon SP, Mehan WA Jr, Conklin J, Huang SY, Lang AL, Giao DM, Leslie-Mazwi TM, Kalpathy-Cramer J, Little BP, Buch K. Severity of Chest Imaging is Correlated with Risk of Acute Neuroimaging Findings among Patients with COVID-19. AJNR Am J Neuroradiol. 2021 May;42(5):831-837. doi: 10.3174/ajnr.A7032. Epub 2021 Feb 4. PMID: 33541897; PMCID: PMC8115353.

  36. Tariq A, Celi LA, Newsome JM, Purkayastha S, Bhatia NK, Trivedi H, Gichoya JW, Banerjee I. Patient-specific COVID-19 resource utilization prediction using fusion AI model. NPJ Digital Medicine. 2021 Jun 3;4(1):1-9.

  37. Wang Y, Tariq A, Khan F, Gichoya JW, Trivedi H, Banerjee I. Query bot for retrieving patients’ clinical history: A COVID-19 use-case. Journal of biomedical informatics. 2021 Nov 1;123:103918.

  38. Fuhrman JD, Chen J, Dong Z, Lure, FYM, Luo Z, Giger ML: Cascaded deep transfer learning on thoracic CT in COVID-19 patients treated with steroids. J. Med. Imag 8(S1), 014501 (2021), doi: 10.1117/1.JMI.8.S1.014501, 2021. [PMID: 33415179]

  39. Hu Q, Drukker K, Giger ML: Role of standard and soft tissue chest radiography images in deep-learning-based early diagnosis of COVID-19, J. Med. Imag. 8, 014503-1 doi: 10.1117/1.JMI.8.S1.014503, 2021.

  40. Fuhrman JD, Gorre N, Hu Q, Li H, El Naqa I, Giger ML: A review of explainable and interpretable AI with applications in COVID-19 imaging, Medical Physics https://doi.org/10.1002/mp.15359, 2021.

  41. Wiggins WF, Kitamura F, Santos I, Prevedello LM. Natural Language Processing of Radiology Text Reports: Interactive Text Classification. Radiol Artif Intell. 2021 May 12;3(4):e210035. doi: 10.1148/ryai.2021210035. PMID: 34350414; PMCID: PMC8328116.

  42. Tsai EB, Simpson S, Lungren M, Hershman M, Roshkovan L, Colak E, Erickson BJ, Shih G, Stein A, Kalpathy-Cramer J, Shen J, Hafez M, John S, Rajiah P, Pogatchnik BP, Mongan J, Altinmakas E, Ranschaert ER, Kitamura FC, Topff L, Moy L, Kanne JP, Wu CC. The RSNA International COVID-19 Open Annotated Radiology Database (RICORD). Radiology. 2021 Jan 05; 203957. PMID: 33399506. PMCID: PMC7993245

  43. El Naqa I, Li H, Fuhrman J, Hu Q, Gorre N, Chen W, Giger ML: Lessons learned in transitioning to AI in the medical imaging of COVID-19, J. Med. Imag. 8(S1), 010902 doi: 10.1117/1.JMI.8.S1.010902, 2021

  44. Arun, Nishanth, Nathan Gaw, Praveer Singh, Ken Chang, Mehak Aggarwal, Bryan Chen, Katharina Hoebel et al. "Assessing the trustworthiness of saliency maps for localizing abnormalities in medical imaging." Radiology: Artificial Intelligence 3, no. 6 (2021): e200267. https://pubmed.ncbi.nlm.nih.gov/34870212/

Editorials.

  1. Gichoya, Judy W. , Rui C. Sá, Ronald M. Summers, Heather Whitney, "Special Section Guest Editorial: Global Health, Bias, and Diversity in AI in Medical Imaging," J. Med. Imag. 10(6) 061101 (10 January 2024) https://doi.org/10.1117/1.JMI.10.6.061101

Preprints.

  1. Zhou, Yiliang, Hanley Ong, Patrick Kennedy, Carol Wu, Jacob Kazam, Keith Hentel, Adam Flanders, George Shih, and Yifan Peng. "Evaluating GPT-4 with Vision on Detection of Radiological Findings on Chest Radiographs." arXiv preprint arXiv:2403.15528 (2024).

  2. Clunie, David A., Adam Flanders, Adam Taylor, Brad Erickson, Brian Bialecki, David Brundage, David Gutman et al. "Report of the Medical Image De-Identification (MIDI) Task Group--Best Practices and Recommendations." arXiv preprint arXiv:2303.10473 (2023).

  3. Yu, Feiyang, et al. Evaluating Progress in Automatic Chest X-Ray Radiology Report Generation, 2022, https://doi.org/10.1101/2022.08.30.22279318. 

  4. Ayis Pyrros, Brian Fornelli, Jorge Mario Rodríguez-Fernández, Stephen M. Borstelmann, Nasir Siddiqui, William Galanter, 2023. Medicare Radiology Group Network Market Share: Recent Trends and Characteristics. medRxiv 2023.03.12.23287068; doi: https://doi.org/10.1101/2023.03.12.23287068

  5. Chambon, P., Bluethgen, C., Langlotz, C.P. and Chaudhari, A., 2022. Adapting pretrained vision-language foundational models to medical imaging domains. arXiv preprint arXiv:2210.04133.

  6. Chambon, P., Bluethgen, C., Delbrouck, J.B., Van der Sluijs, R., Połacin, M., Chaves, J.M.Z., Abraham, T.M., Purohit, S., Langlotz, C.P. and Chaudhari, A., 2022. RoentGen: Vision-Language Foundation Model for Chest X-ray Generation. arXiv preprint arXiv:2211.12737.

  7. Jain, Saahil, et al. "Radgraph: Extracting clinical entities and relations from radiology reports." arXiv preprint arXiv:2106.14463 (2021).

  8. Banerjee I, Bhimireddy AR, Burns JL, Celi LA, Chen LC, Correa R, Dullerud N, Ghassemi M, Huang SC, Kuo PC, Lungren MP,Palmer L, Price B, Purkayastha S, Pyrros A, Oakden-Rayner L, Okechukwu C, Seyyed-Kantari L, Trivedi H, Wang R, Zaiman Z, Zhang H, Gichoya JW. Reading Race: AI Recognises Patient's Racial Identity In Medical Images. arXiv preprint arXiv:2107.10356. 2021 Jul 21.

  9. Sun J, Peng L, Li T, Adila D, Zaiman Z, Melton GB, Ingraham N, Murray E, Boley D, Switzer S, Burns JL. A Prospective Observational Study to Investigate Performance of a Chest X-ray Artificial Intelligence Diagnostic Support Tool Across 12 US Hospitals. arXiv preprint arXiv:2106.02118. 2021 Jun 3.

  10. cOkechukwu C, Seyyed-Kantari L, Trivedi H, Wang R, Zaiman Z, Zhang H, Gichoya JW. Reading Race: AI Recognises Patient's Racial Identity In Medical Images. arXiv preprint arXiv:2107.10356. 2021 Jul 21.

  11. Tariq A, Tang S, Sakhi H, Celi LA, Newsome JM, Rubin DL, Trivedi H, Wawira Gichoya J, Banerjee I; Fusion of Imaging and Non-Imaging Data for Disease Trajectory Prediction for COVID-19 Patients, medRxiv 2021.12.02.21267211; doi: https://doi.org/10.1101/2021.12.02.21267211

  12. Jain S, Agrawal A, Saporta A, Truong SQH, Duong DN, Bui T, Chambon P, Zhang Y, Lungren MP, Ng AY, Langlotz CP, Rajpurkar P; ‘RadGraph: Extracting Clinical Entities and Relations from Radiology Reports.’, arXiv, 2021, arXiv.org

  13. Cohen, Joseph Paul and Brooks, Rupert and En, Sovann and Zucker, Evan and Pareek, Anuj and Lungren, Matthew P. and Chaudhari, Akshay, Gifsplanation via Latent Shift: A Simple Autoencoder Approach to Counterfactual Generation for Chest X-rays, arXiv, 2021, 10.48550/ARXIV.2102.09475.

  14. Li MD, Arun NT, Gidwani M, Chang K, Deng F, Little BP, Mendoza DP, Lang M, Lee SI, O'Shea A, Parakh A, Singh P, Kalpathy-Cramer J. Automated assessment of COVID-19 pulmonary disease severity on chest radiographs using convolutional Siamese neural networks. medRxiv [Preprint]. 2020 May 26:2020.05.20.20108159. doi: 10.1101/2020.05.20.20108159. Update in: Radiol Artif Intell. 2020 Jul 22;2(4):e200079. PMID: 32511570; PMCID: PMC7274251.

  15. Aggarwal, Mehak and Arun, Nishanth and Gupta, Sharut and Vaswani, Ashwin and Chen, Bryan and Li, Matthew and Chang, Ken and Patel, Jay and Hoebel, Katherine and Gidwani, Mishka and Kalpathy-Cramer, Jayashree and Singh, Praveer; Towards Trainable Saliency Maps in Medical Imaging, arXiv, 2020, 10.48550/ARXIV.2011.07482

Conference proceedings.

Selection only

  1. Shenouda M, Kaveti A, Flerlage I, Kalpathy-Cramer J, Giger ML, Armato SG III: Assessing robustness of a deep-learning model for COVID-19 classification on chest radiographs. Proceedings SPIE 12465: 124650F, 2023

  2. Hu Q, Drukker K, Giger ML. Predicting the Need for Intensive Care for COVID-19 Patients using Deep Learning on Chest Radiography. The 34th Neural Information Processing Systems Conference, Medical Imaging meets NeurIPS Workshop 2020.

  3. Hu Q, Drukker K, Giger ML. Role of standard and soft tissue chest radiography images in COVID-19 diagnosis using deep learning. InMedical Imaging 2021: Computer-Aided Diagnosis 2021 Feb 15 (Vol. 11597, p. 1159704). International Society for Optics and Photonics. (Runner-up, Computer-Aided Diagnosis Paper Award. Runner-up, Robert F. Wagner All-Conference Best Student Paper award.)

  4. Patel N, D'Silva K, Li M, Hsu T, Di Iorio M, Fu X, Cook C, Prisco L, Martin L, Vanni K, Zaccardelli A, Zhang Y, Kalpathy-Cramer J, Sparks J, Wallace Z. Deep Learning-Derived Chest Radiograph Scores in COVID-19 in Rheumatic Disease Patients versus General Population Comparators [abstract]. Arthritis Rheumatol. 2021; 73 (suppl 10). https://acrabstracts.org/abstract/deep-learning-derived-chest-radiograph-scores-in-covid-19-in-rheumatic-disease-patients-versus-general-population-comparators/.

Grants.

  1. DOE “PALISADE-X: Privacy-Preserving Analysis and Learning in Secure and Distributed Enclaves and Exascale Systems”, Ravi Madduri, PI, Subcontract to Maryellen Giger, 08/02/2020-08/01/2021, Total cost $1M, subcontract total cost $118,000.

Awards.

  1. Natalie Baughman SPIE Medical Imaging 2022 - Robert F. Wagner SPIE Medical Imaging Best Student Paper Award Finalist

Under review.

  1. Chambon et al., Improved fine-tuning of in-domain transformer model for inferring COVID-19 presence in multi-institutional radiology reports

  2. Gichoya et al., A Prospective Observational Study to Investigate Performance of a Chest X-ray Artificial Intelligence Diagnostic Support Tool Across 12 US Hospitals – submitted to Radiology:AI

  3. Gichoya et al., Fusion of Imaging and Non-Imaging Data for Disease Trajectory Prediction for COVID-19 Patients

Other.

Selection only

  1. Giger MLG., “MIDRC: AAPM Leadership in Medical Imaging and Data Science”, oral presentation, AAPM annual meeting 2022

  2. Baughan N,, "Task-Based Sampling of the MIDRC Sequestered Data Commons for Algorithm Performance Evaluation", oral presentation, authors: N. Baughan, University of Chicago; H. Whitney, Wheaton College; K. Drukker, University of Chicago; B. Sahiner, US Food & Drug Administration; T. Hu, US Food & Drug Administration; G. Kim, University of California Los Angeles; M. McNitt-Gray, University of California Los Angeles; K. Myers, US Food & Drug Administration, retired; M. Giger, University of Chicago), oral presentation, AAPM annual meeting 2022

  3. Giger MLG, “Medical Physics in Non-Cancer Imaging and Therapy & Data Resource”, Dr. M. Giger on behalf of Dr. K. Kandarpa (NIBIB), oral presentation, AAPM annual meeting 2022

  4. Drukker K, “Towards Fairness in Artificial Intelligence for Medical imaging: Identification and Mitigation of Biases in the Roadmap from Data commons to Model Design and Deployment”, ePoster on behalf of the Bias and Diversity Working Group, authors: K. Drukker, University of Chicago; W. Chen, US Food & Drug Administration; J. Gichoya, Emory University; N. Gruszauskas, University of Chicago; J. Kalpathy-Cramer, University of Colorado; S. Koyejo, Stanford University; K. Myers, US Food & Drug Administration, retired; B. Sahiner, US Food & Drug Administration; H. Whitney, Wheaton College; Z. Zhang, Jefferson Health; M. Giger, University of Chicago, AAPM annual meeting 2022

  5. Whitney H, “Evaluation of Diversity in MIDRC Open Data Commons”, Dr. Heather Whitney on behalf of the Technology Development Project 3d and the Bias and Diversity Working Group, authors: H. Whitney, Wheaton College; N. Baughan, University of Chicago; K. Drukker, University of Chicago; K. Myers, US Food & Drug Administration, retired; M. Giger, University of Chicago), ePoster, AAPM annual meeting 2022

  6. McNitt-Gray M., “Developing Tools to Assist in Task-Specific Performance Evaluation for Machine Learning Algorithms”, Dr. Michael McNitt-Gray on behalf of Technology Development Project 3c, authors: K. Drukker, University of Chicago; B. Sahiner, US Food & Drug Administration; T. Hu, US Food & Drug Administration; G. Kim, University of California Los Angeles; H. Whitney, Wheaton College; N. Baughan, University of Chicago; K. Myers, US Food & Drug Administration, retired; M. Giger, University of Chicago; M. McNitt-Gray, University of California Los Angeles, ePoster, AAPM annual meeting 2022

  7. Shusharina N., “Anatomy-Specific Classification of Medical Imaging Data Hosted by Medical Imaging and Data Resource Center (MIDRC)”, Dr. Nadya Shusharina on behalf of Collaborative Research Project 12, authors: N Shusharina1, Massachusetts General Hospital; D Krishnaswamy, Brigham & Women's Hospital, Boston, MA,; P Kinahan, University of Washington, Seattle, WA; A Fedorov, Brigham & Women's Hospital, Boston, MA, ePoster, AAPM annual meeting 2022

  8. Tariq A, Gichoya JW. COVID-19 resource utilization prediction using fusion AI model. RSNA annual meeting, Nov 29th, 2021, Chicago, IL (poster)

  9. Tariq A, Celi L, Newsome J, Purkayastha S, Bhatia N, Merchant F, Trivedi H, Gichoya JW, Banerjee I. “Patient-specific COVID-19 Resource Utilization Prediction Using Fusion AI model”, American College of Cardiology (ACC) 70th Annual Scientific Session, Atlanta, May 15-17, 2021. (flatboard poster) (Virtual)

  10. Medical Imaging and Data Resource Center: A Multi-Society Approach to Advance Research on COVID-19. Radiological Society of North America Annual Meeting, Chicago, Illinois. December 2021

  11. Pyrros A, Gichoya JW. Detection of racial/ethnic health disparities in COVID-19 patients using a deep learning chest radiography classifier. RSNA annual meeting, Nov 29th, 2021, Chicago, IL (poster)

  12. Zaiman Z, Sinha P, Okechukwu C, Pyrros A, Trivedi H, Banerjee I, Gichoya JW. Clinical performance of COVID-19 CXR prediction models across multiple institutions. RSNA annual meeting, Nov 29th, 2021, Chicago, IL (poster)

  13. Gichoya JW*. 2020 year in review: AI versus COVID? Applied Machine Learning Days annual conference, June 28th, 2021. (oral) (Virtual)

  14. Zaiman Z*, Okechukwu C, Banerjee I, Gichoya J. Binary classification of COVID-19 based on frontal radiographs. Society of Imaging Informatics (SIIM) Annual Meeting. May 24th, 2021. (oral) (Virtual)

  15. Jordan D. Fuhrman, Chaojie Wei, Longjun He, Hui Li, Zhe Luo, Zegang Dong, Fleming Y. M. Lure, Zhenshun Cheng, Maryellen L Giger. Validation of deep transfer learning on CT scans for informing steroid treatments of 864 COVID-19 patients. 107th Assembly and Annual Meeting of Radiological Society of North America (RSNA), Chicago, Illinois, Dec 2021

  16. Hu Q, Drukker K, Giger ML. Role of standard and soft tissue chest radiography images in COVID-19 diagnosis using deep learning. SPIE Medical Imaging 2021.

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