2023

Hsieh C, Laguna A, Ikeda I, Maxwell AW, Chapiro J, Nadolski G, Jiao Z, Bai HX. Using Machine Learning to Predict Response to Image-guided Therapies for Hepatocellular Carcinoma. Radiology. 2023 Nov 7;309(2):e222891.

Zhong Z, Li J, Kulkarni S, Li Y, Fayad FH, Zhang H, Ahn SH, Bai H, Gao X, Atalay MK, Jiao Z. Improving Outcome Prediction of Pulmonary Embolism by De-biased Multi-modality Model. InInternational Conference on Medical Image Computing and Computer-Assisted Intervention 2023 Oct 1 (pp. 515-525). Cham: Springer Nature Switzerland.

Zhou H, Bai HX, Jiao Z, Cui B, Wu J, Zheng H, Yang H, Liao W. Deep learning-based radiomic nomogram to predict risk categorization of thymic epithelial tumors: A multicenter study. European Journal of Radiology. 2023 Nov 1;168:111136.

Ren Z, Li J, Xue X, Li X, Yang F, Jiao Z, Gao X. Reconstructing controllable faces from brain activity with hierarchical multiview representations. Neural Networks. 2023 Sep 1;166:487-500.

Zhao LM, Zhang H, Kim DD, Ghimire K, Hu R, Kargilis DC, Tang L, Meng S, Chen Q, Liao WH, Bai H. Head and neck tumor segmentation convolutional neural network robust to missing PET/CT modalities using channel dropout. Physics in Medicine & Biology. 2023 Apr 25;68(9):095011.

Tian C, Zhang Z, Gao X, Zhou H, Ran R, Jiao Z. An Implicit-Explicit Prototypical Alignment Framework for Semi-Supervised Medical Image Segmentation. IEEE Journal of Biomedical and Health Informatics. 2023 Nov 6.

Yue H, Geng J, Gong L, Li Y, Windsor G, Liu J, Pu Y, Du Y, Wang R, Wu H, Jiao Z. Radiation hematologic toxicity prediction for locally advanced rectal cancer using dosimetric and radiomics features. Medical physics. 2023 Feb 13.

Hou B, Li H, Jiao Z, Zhou Z, Zheng H, Fan Y. Deep Clustering Survival Machines with Interpretable Expert Distributions. 2023 International Symposium on Biomedical Imaging.

Khunte M, Chae A, Wang R, Jain R, Sun Y, Sollee JR, Jiao Z, Bai HX. Trends in clinical validation and usage of US Food and Drug Administration-cleared artificial intelligence algorithms for medical imaging. Clinical Radiology. 2023 Feb 1;78(2):123-9.

Windsor GO, Bai H, Lourenco AP, Jiao Z. Application of Artificial Intelligence in Predicting Lymph Node Metastasis in Breast Cancer. Frontiers in Radiology.;3:2.

2022

 

  • Meng S, Tran TML, Hu M, Wang P, Yi T, Zhong Z, Wang L, Vogt B, Jiao Z, Barman A, Cetintemel U,  Chang K, Nguyen DT, Hui FK, Pan I, Xiao B, Yang L, Zhou H, Bai HX. “End-to-end artificial intelligence platform for the management of large vessel occlusions: a preliminary study.” Journal of Stroke and Cerebrovascular Diseases, 2022.
  • Li Y, Zou B, Wu J, Dai Y, Bai HX, Jiao Z. A dynamic multi-modal fusion network for ovarian tumor differentiation. In2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2022 Dec 6 (pp. 767-772). IEEE.
  • Li Y, Zou B, Dai Y, Zhu C, Yang F, Li X, Bai HX, Jiao Z. Parameter-Free Latent Space Transformer for Zero-Shot Bidirectional Cross-modality Liver Segmentation. InMedical Image Computing and Computer Assisted Intervention–MICCAI 2022: 25th International Conference, Singapore, September 18–22, 2022, Proceedings, Part IV 2022 Sep 16 (pp. 619-628). Cham: Springer Nature Switzerland.

ONLINE:

  • Li J, Liu J, Li J, Yue H, Lang J, Cheng J, Kuang H, Bai HX, Wang Y, Wang J. “DARC: Deep adaptive regularized clustering for histopathological image classification.” Medical Image Analysis, 2022.
  • Yue H, Liu J, Li J, Kuang H, Lang J, Cheng J, Peng L, Han Y, Bai HX, Wang Y, Wang Q, Wang J. “MLDRL: Multi-loss disentangled representation learning for predicting esophageal cancer response to neoadjuvant chemoradiotherapy using longitudinal CT images.” Medical Image Analysis, 2022.
  • Chapiro J, Allen B, Abajian A, Wood B, Kothary N, Daye D, Bai HX, Sedrakyan A, Diamond M, Simonyan V, McLennan G, Abi-Jaoudeh N, Pua B. “Proceedings from the Society of Interventional Radiology Foundation Research Consensus Panel on Artificial Intelligence in Interventional Radiology: From Code to Bedside”. Journal of Vascular and Interventional Radiology, 2022.
  • Huang B, Sollee J, Luo Y, Reddy A, Zhong Z, Wu J, Mammarappallil J, Healey T, Cheng G, Azzoli C, Korogodsky D, Zhang P, Feng X, Li J, Yang L, Jiao Z, Bai HX. “Prediction of lung malignancy progression and survival with machine learning based on pre-treatment FDG-PET/CT”. eBioMedicine, 2022.
  • Hu R,  Li H, Horng H, Thomasian N, Jiao Z, Zhu C, Zou B, Bai HX. “Automated machine learning for differentiation of hepatocellular carcinoma from intrahepatic cholangiocarcinoma on multiphasic MRI”. Scientific Reports, 2022.
  • Zhang Z, Tian C, Bai HX, Jiao Z, Tian X. “Discriminative error prediction network for semi-supervised colon gland segmentation”. Medical Image Analysis, 2022.
  • Sollee J, Tang L, Igiraneza A, Xiao B, Bai HX, Yang L. “Artificial Intelligence for Medical Image Analysis in Epilepsy “. Epilepsy Research, 2022.
  • Cheng J, Sollee J, Hsieh C, Yue H, Vandal N, Shanahan J,  Choi JW,  Tran TML, Halsey K, Iheanacho F, Warren J, Ahmed A, Eickhoff C, Feldman M, Barbosa EJM,  Kamel I, Lin CT, Yi TY, Healey T, Zhang P, Wu J, Atalay M, Bai HX,  Jiao Z, Wang J. “COVID-19 mortality prediction in the intensive care unit with deep learning based on longitudinal chest X-rays and clinical data”. European Radiology, 2022.
  • Eweje FR, Byun S, Chandra R, Hu F, Kamel I, Zhang P, Jiao Z, Bai HX. “Translatability analysis of National Institutes of Health-funded biomedical research that applies artificial intelligence”. JAMA Network Open, 2022.
  • Thomasian N, Kamel I, Bai HX. “Machine intelligence in noninvasive endocrine cancer diagnostics.” Nature Reviews Endocrinology, 2022 (featured as a cover article)
  • Kim CK, Choi JW, Jiao Z, Wang D, Wu J, Yi TY, Halsey KC, Eweje F, Tran TML, Chang L, Wang R, Sollee J, Hsieh S, Chang K, Yang FX, Singh R, Ou JL, Huang RY, Feng C, Feldman MD, Liu T, Gong JS, Lu SL, Eickhoff C, Feng X, Kamel I, Sebro R, Atalay MK, Healey T, Fan Y, Liao WH, Wang JX, Bai HX. “An automated pipeline for rapid triage of COVID-19 patients using artificial intelligence based on chest radiographs and clinical data”. NPJ Digital Medicine, 2022

2021

ONLINE:

  • Jiao Z, Li H, Xiao Y, Dorsey J, Simone CB, Feigenberg S, Kao G, Fan Y. “Integration of deep learning radiomics and counts of circulating tumor cells improves prediction of outcomes of early stage NSCLC patients treated with SBRT”. International Journal of Radiation Oncology, Biology, Physics, 2021
  • Hu R, Li D, Wu J, Li H, Cai Y, Zhang PJ, Zhu C, Bai HX. “Performance of automatic machine learning versus radiologists in the evaluation of endometrium on computed tomography.” Abdominal Radiology, 2021
  • Yi T, Pan I, Collins S, Chen F, Cueto R, Hsieh B, Hsieh C, Smith JL, Yang L, Liao WH, Merck LH, Bai HX, Merck D. “DICOM Image ANalysis and Archive (DIANA): an Open-Source System for Clinical AI Applications”. Journal of Digital Imaging, 2021.
  • Peng J, Kim DD, Patel JB, Zeng X, Huang J, Chang K, Xun X, Zhang C, Sollee J, Wu J, Dalal DJ, Feng X, Zhou H, Zhu C, Zou B, Jin K, Wen PY, Boxerman JL, Warren KE, Poussaint TY, States LJ, Kalpathy-Cramer J, Yang L, Huang RY, Bai HX. “Deep learning-based automatic tumor burden assessment of pediatric high-grade gliomas, medulloblastomas, and other leptomeningeal seeding tumors.” Neuro-Oncology, 2021
  • Wang R, Jiao Z, Yang L, Choi JW, Xiong Z, Halsey K, Tran TML, Pan I, Collins SA, Feng X, Wu J, Chang K, Shi LB, Yang S, Yu QZ, Liu J, Fu FX, Jiang XL, Wang DC, Zhu LP, Yi XP, Healey TT, Zeng QH, Liu T, Hu PF, Huang RY, Li YH, Sebro RA, Zhang PJL, Wang J, Atalay MK, Liao WH, Fan Y, Bai HX. “Artificial intelligence for prediction of COVID-19 progression using CT imaging and clinical data.” European Radiology, 2021
  • Jiao Z, Choi JW, Halsey K, Tran TML, Hsieh B, Wang D, Eweje F, Wang R, Chang K, Wu J, Collins SA, Yi T, Delworth A, Liu T, Healey TT, Lu S, Wang J, Feng X, Atalay MK, Li Y, Liao WH, Fan Y, Bai HX. Prognostication of COVID-19 patients presenting to the emergency department using artificial intelligence based on chest radiographs and clinical data.” The Lancet Digital Health, 2021
  • Purkayastha S, Xiao Y, Jiao Z, Thepumnoeysuk R, Halsey K, Wu J, Tran TML, Hsieh B, Choi JW, Wang D, Vallières M, Wang R, Collins S, Feldman XFM, Zhang PJ, Atalay M, Sebro R, Yang L, Fan Y, Liao WH, Bai HX. “Machine learning to predict COVID-19 severity and progression to critical illness using CT imaging and clinical data.” Korean Journal of Radiology, 2021
  • Eweje FR, Bao B, Wu J, Dalal D, Liao WH, He Y, Luo YH, Lu SL, Zhang P, Sebro RA, Peng XJ, Bai HX, States L. “Deep Learning for Classification of Bone Lesions on Routine MRI Article.” EBioMedicine, 2021
  • Kanne J, Bai HX, Bernheim A, Chung M, Haramati L, Kallmes D, Little B, Rubin G, Sverzellati N. “COVID-19 Imaging: What We Know Now and What Remains Unknown.” Radiology, 2021

2020

ONLINE:

  • Wang R, Cai Y, Lee IK, Hu R, Purkayastha S, Pan I, Yi T, Tran TML, Lu S, Liu T, Change K, Huang RY, Zhang PJ, Zhang Z, Xiao E, Wu J, Bai HX. “Evaluation of a Convolutional Neural Network for Ovarian Tumor Differentiation Based on Magnetic Resonance Imaging.” European Radiology,
    2020.
  • Purkayastha S, Zhao Y, Wu J, Hu R, McGirr A, Singh S, Chang K, Huang RY, Zhang PJ, Silva A, Soulen  MC, Stavropoulos SW, Zhang Z, Bai HX. “Differentiation of low and high grade renal cell carcinoma on routine MRI with an externally validated automatic machine learning algorithm.” Scientific Reports, 2020. 
  • He Y, Pan I, Bao B, Halsey K, Chang M, Liu H, Peng  S, Sebro RA, Guan J, Yi T, Delworth AT, Eweje F, States LJ, Zhang PJ, Zhang Z, Wu J, Peng X, Bai HX. “Deep Learning-Based Classification of Primary Bone Tumors on Radiographs: A Preliminary StudyDeep Learning-Based Classification of Primary Bone Tumors on Radiographs: A Preliminary Study Article (Original Research).” EBioMedicine, 2020.
  • Bai HX, Thomasian NM. “RICORD: A Precedent for Open AI in COVID-19 Image Analytics.” Radiology, 2020.
  • Choi JH , Hu R, Zhao Y, Purkayastha S, Wu J, McGirr AJ, Stavropoulos SW, Silva AC, Soulen MC, Paler MB, Zhang PJL, Zhu C, Ahn SH, Bai HX. “Preoperative prediction of the stage, size, grade, and necrosis score in clear cell renal cell carcinoma using MRI-based radiomics.” Abdominal Radiology, 2020.
  • Cheng J, Liu J, Yue H, Bai HX, Yi P, Wang J. “Prediction of Glioma Grade using Intratumoral and Peritumoral Radiomic Features from Multiparametric MRI Images.” IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2020.
  • Xi I, Wu J, Guan J, Zhang PJ, Horri SC, Soulen MC, Zhang Z, Bai HX. “Deep learning for differentiation of benign and malignant solid liver lesions on ultrasonography.” Abdominal Radiology, 2020 Jul 17. doi: 10.1007/s00261-020-02564-w.
  • Zhou H, Hu R, Tang O, Hu C, Tang L, Chang K, Shen Q, Wu J, Zou B, Xiao B, Boxerman J, Chen W,  Huang RY, Yang L, Bai HX, Zhu C. “Automatic machine learning to differentiate pediatric posterior fossa tumors on routine magnetic resonance imaging.” American Journal of Neuroradiology, July 2020. doi: https://doi.org/10.3174/ajnr.A6621.
  • Bai HX, Wang R, Xiong Z, Hsieh B, Chang K, Halsey K, Tran TML, Choi JW, Wang DC, Shi LB, Mei J, Jiang XL, Pan I, Zeng QH, Hu PF, Li YH, Fu, FX, Huang RY, Sebro R, Yu QZ, Atalay MK, Liao W. “AI Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Etiology on Chest CT.” Radiology, 2020 April 27. doi: 10.1148/radiol.2020201491.
  • Collins SA, Wu J, Bai HX. “Facial de-identification of head CT scans.” Radiology, 2020 Apr 7. doi: 10.1148/radiol.2020192617.
  • Xi IL, Zhao Y, Wang R, Chang M, Purkayastha S, Chang K, Huang RY, Silva AC, Vallières M, Habibollahi P, Fan Y, Zou B, Gade TP, Zhang PJ, Soulen MC, Zhang Z, Bai HX, Stavropoulos SW. “Deep learning to distinguish benign from malignant renal lesions based on routine MR imaging.”  Clinical Cancer Research, 2020 Jan 14. doi: 10.1158/1078-0432.
  • Zhao Y, Chang M, Wang R, Xi IL, Chang K, Huang RY, Vallières M, Habibollahi P, Dagli MS, Palmer M, Zhang PJ, Silva AC, Yang L, Soulen MC, Zhang Z, Bai HX, Stavropoulos SW. “Deep learning based on MRI for differentiation of low- and high-grade in low-stage renal cell carcinoma.” Journal of Magnetic Resonance Imaging, 2020 Mar 28. doi: 10.1002/jmri.27153.

  • Luo YH, Xi IL, Wang R, Abdallah HO, Wu J, Vance AZ, Chang K, Kohi M, Jones L, Reddy S, Zhang ZS, Bai HX, Shlansky-Goldberg R. “Deep learning based on MR imaging for predicting outcome of uterine fibroid embolization.” Journal of Vascular and Interventional Radiology. 2020 May 3. doi: 10.1016/j.jvir.2019.11.032.

2019

  • Zhou H, Chang K, Bai HX, Xiao Bo, Su C, Bi WL, Zhang PJ, Senders JT, Kavouridis VK, Boaro A, Arnaout O, Yang L, Huang RY. “Machine learning reveals multimodal MRI patterns predictive of isocitrate dehydrogenase genotype in diffuse lower-grade and high-grade glioma.” J Neurooncol. 2019 Jan 19. doi: 10.1007/s11060-019-03096-0.
  • Chang K, Beers AL, Bai HX, Brown JM, Ly KI, Li X, et al. “Automatic assessment of glioma burden: a deep learning algorithm for fully automated volumetric and bidimensional measurement.” Neuro Oncol. 2019 Nov 4; 21(11): 1412-1422. doi: 10.1093/neuonc/noz106.

2018

  • Chang K, Bai HX, Hao Z, Su C, Bi WL, Agbodza E, Kavouridis VK, Senders JT, Boaro A, Beers A, Zhang B, Capellini A, Liao W, Shen Q, Li X, Xiao B, Cryan J, Ramkissoon L, Ligon K, Wen PY, Bindra R, Woo J, Arnaout O, Zhang PJ, Rosen BR, Yang L, Huang RY, Kalpathy-Cramer J. “Residual Convolutional Neural Network for determination of IDH status in low- and high-grade gliomas from MR imaging.” Clin Cancer Res. 2018 Mar 1. 24(5):1073-1081 doi: 10.1158/1078-0432.CCR-17-2236.
  • Kuthuru S, Deaderick W, Bai HX, Su C, Vu T, Monga V, Rao A. “Visually interpretable approach to imaging-genomic modeling.” Cancer Inform. 2018 Oct 5;17:1176935118802796. doi: 10.1177/1176935118802796.

2017

  • Zhou H, Vallières M, Bai HX4, Su C, Tang H, Oldridge D, Zhang Z, Xiao B, Liao W, Tao Y, Zhou J, Zhang P, Yang L. “MRI features predict survival and molecular markers in diffuse lower-grade gliomas.” Neuro Oncology. 2017 Jun 1; 19(6): 862-870. doi: 10.1093/neuonc/now256.

2016

  • Tang H, Bai HX, Su C, Lee AM, Yang L. “The effect of cirrhosis on radiogenomic biomarker’s ability to predict microvascular invasion and outcome in hepatocellular carcinoma.” Hepatology. 2016 Aug; 64(2): 691-2. doi: 10.1002/hep.28620.
  • Bai HX, Lee AM, Yang L, Zhang P, Davatzikos C, Maris JM, Diskin SJ. “Imaging genomics in cancer research: limitations and promises.” The British Journal of Radiology. 2016; 89(1061): 20151030. doi: 10.1259/bjr.20151030.
  • Yang L, Bai HX, Lee AM. “Leveraging Imperfect Data Sets to Draw New Conclusions: Radiogenomics’ True Value?” Journal of the American Collage Radiology. 2016 Feb; 13(2): 120-1. doi: 10.1016/j.jacr.2015.10.013.

ETC.

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