Welcome To

Computer-aided Diagnosis Lab

Bin Zheng, PhD.

Gerald Tuma Presidential Professor

School of Electrical and Computer Engineering

Oklahoma TSET Cancer Research Scholar

Peggy & Charles Stephenson Cancer Center

University of Oklahoma


Current Research Interest and Topics

      Our research lab focuses on developing and evaluating new computer-aided quantitative medical image processing algorithms and machine learning models with easy-to-use graphic user interface (GUI) tools to help predict disease (i.e., cancer and stroke) risk and prognosis, which include to develop and evaluate new computer-aided detection and/or diagnosis (CAD) schemes to:

  • Predict (a) short-term breast cancer risk using bilateral digital mammograms [1], (b) radiological complete response (rCR) of breast cancer patients to NACT using pre-NACT breast MR images [2], (c) risk of lung cancer recurrence after surgery among the stage I NSCLC patients [3] using the diagnostic chest CT images, (d) progression-free survival (PFS) of ovarian cancer patients after chemotherapy using longitudinal abdomen CT images acquired from pre-therapy and 4-6 weeks on-therapy [4], and (e) risk of metastasis of cervical cancer [5] and gastric cancer patients using CT images [6].
  • Classify between (a) malignant and benign breast lesions using conventional digital mammograms [7], contrast-enhanced digital mammograms [8] and breast MRI [9], (b) malignant and benign lung nodules using CT images [10], and (c) tumor epithelium and stroma using digital histopathology images [11].
  • Improve performance and potential clinical utility of CAD schemes to detect suspicious breast lesions using digital mammograms [12] and lung nodules using chest CT images [13] using new adaptive optimization and result cuing methods.
  • Apply, modify and test deep transfer learning models to (a) segment suspicious tumors and other regions of interest (ROIs) depicting on CT and digital histopathology images [14], and (b) classify between malignant and benign tumors including breast masses using digital mammograms [15] and lung nodules using chest CT images [16].
  • Quantify severity and/or predict treatment efficacy of other diseases including acute ischemic or hemorrhagic stroke [17], and COVID-19 infected pneumonia [18]).

Cited References

  1. Tan M, Zheng B, Leader JK, Gur D, Association between changes in mammographic image features and risk for near-term breast cancer development, IEEE Transactions on Medical Imaging, 2016; 35:1719-1728.
  2. Aghaei F, Tan M, Hollingsworth AB, Zheng B, Applying a new quantitative global breast MRI feature analysis scheme to assess tumor response to chemotherapy, Journal of Magnetic Resonance Imaging, 2016; 44:1099-1106.
  3. Emaminejad N, Qian W, Guan Y, Tan M, Qiu Y, Liu H, Zheng B, Fusion of quantitative image features and genomic biomarkers to improve prognosis assessment of early stage lung cancer patients, IEEE Transactions on Biomedical Engineering, 2016; 63:1034-1043.
  4. Khuzani AZ, Du Y, Heidari M, Thai T, Gunderson C, Moore K, Mannel R, Liu H, Zheng B, Qiu Y, Prediction of chemotherapy response in ovarian cancer patients using a new clustered quantitative image marker, Physics in Medicine and Biology 2018; 63:155020.
  5. Chen X, Liu W, Thai TC, Castellano T, Gunderson CC, Moore K, Mannel RS, Liu H, Zheng B, Qiu Y, Developing a new radiomics-based CT image marker to detect lymph node metastasis among cervical cancer patients, Computer Methods and Programs in Biomedicine 2020; 197:105759.
  6. Mirniaharikandehei S, Heidari M, Danala G, Lakshmivarahan S, Zheng B, Applying a random projection algorithm to optimize machine learning model for predicting peritoneal metastasis in gastric cancer patients using CT images, Computer Methods and Programs in Biomedicine, 2021; 200:105937.
  7. Heidari M, Mirniaharikandehei S, Liu W, Hollingsworth AB, Liu H, Zheng B, Development and assessment of a new global mammographic image feature analysis scheme to predict likelihood of malignant cases, IEEE Transactions on Medical Imaging 2020; 39:1235-1244.
  8. Danala G, Patel B, Aghaei F, Heidari M, Li J, Wu T, Zheng B, Classification of breast masses using a computer-aided diagnosis scheme of contrast enhanced digital mammograms, Annals of Biomedical Engineering, 2018; 46:1419-1431.
  9. Fan M, Li H, Wang S, Zheng B, Zhang J, Li L, Radiomic analysis reveals DCE-MRI features for prediction of molecular subtypes of breast cancer, PLoS One 2017; 12(2):e0171683.
  10. Gong J, Liu J, Sun X, Zheng B, Nie S, Computer-aided diagnosis of lung cancer: The effect of training datasets on classification accuracy of lung nodules, Physics in Medicine and Biology, 2018; 63:035036.
  11. Du Y, Zhang R, Khuzani AZ, Thai T, Gunderson C, Moxley, K, Liu H, Zheng B, Qiu Y, Classification of tumor epithelium and stroma by exploiting image features learned by deep convolutional neural networks, Annals of Biomedical Engineering 2018; 46: 1988-1999.
  12. Tan M, Aghaei F, Wang Y, Zheng B, Developing a new case based computer-aided detection scheme and an adaptive cueing method to improve performance in detecting mammographic lesions, Physics in Medicine and Biology 2017; 62:358-376.
  13. Gong J, Liu J, Wang L, Sun X, Zheng B, Nie S, Automatic detection of pulmonary nodules in CT images by incorporating 3D tensor filtering with local image feature analysis, Physica Medica 2018; 46:124-133.
  14. Shi T, Jiang H, Zheng B, A stacked generalized U-shape network based on zoom strategy and its application in biomedical image segmentation, Computer Methods and Programs in Biomedicine 2020; 197:105678.
  15. Qiu Y, Yan S, Gundreddy R, Wang Y, Cheng S, Liu H, Zheng B, A new approach to develop computer-aided diagnosis scheme of breast mass classification using deep learning technology, Journal of X-ray Science and Technology 2017; 25:751-763.
  16. Gong J, Liu J, Hao W, Nie S, Zheng B, Wang S, Peng W, A deep residual learning network for predicting lung adenocarcinoma manifesting as ground-glass nodule on CT images, European Radiology 2020; 30:1847-1855.
  17. Santucci J, Ross S, Greemert J, Aghaei F, Ford L, Hollabaugh K, Conwell B, Wu D, Zheng B, Bohnstedt B, Ray B, Radiological estimation of intracranial blood volume and occurrence of hydrocephalus determines stress-induced hyperglycemia after aneurysmal subarachnoid hemorrhage, Translational Stroke Research 2019; 10:327-337.
  18. Heidari M, Mirniaharkandehei S, Khuzani AZ, Danala G, Qiu Y, Zheng B, Improving the performance of CNN to predict the likelihood of COVID-19 using chest X-ray images with preprocessing algorithms, International Journal of Medical Informatics 2020; 144:104284.