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Breast Imaging

The breast imaging research group focuses on the clinical application of novel techniques using Digital Breast Tomosynthesis (DBT), Magnetic Resonance Imaging (MRI) and Ultrasound (US) to enhance screening and diagnosis of patients with breast diseases. Some high impact research projects completed in the past include abbreviated breast MRI for screening women with dense breasts, automated whole breast ultrasound and opto-acoustic technology for evaluation of breast lesions and cancer diagnosis. Presently, some exciting research projects are taking place at the breast center, such as a breast imaging MRI contrast study evaluating a new contrast agent for the market, imaging of breast core biopsies to improve efficiency of the biopsy process, Artificial Intelligence (AI) projects that will help with diagnosis accuracy, a PET/MRI imaging study for the Pathologic Complete Response (PTC) response of etc.

In their effort to continually improve patient care, breast imaging scientists from the Department of Radiology collaborate with colleagues across Robert H. Lurie Cancer Center, Feinberg School of Medicine and Northwestern University, including experts in oncology, oncologic surgery, biomedical engineering, preventive medicine and dermatology.

 Ulas Bagci, PhD

Associate Professor of Radiology (Basic and Translational Radiology Research)

Bio

Dr. Ulas Bagci is the director of The Machine & Hybrid Intelligence Lab, and an Associate Professor (with tenure) in the Department of Radiology in the Feinberg School of Medicine, and theDepartment of ECE and Biomedical Engineering, at Northwestern. Dr. Bagci has a broad background in Artificial Intelligence (AI) and Machine Learning with specific training in biomedical imaging in internationally renowned research centers. 
Dr. Bagci has more than 400 peer-reviewed articles, and holds several NIH grants (R01, U01, P50, R21 and others). Dr. Bagci is an investigator at the Lurie Cancer Center, an external member of NIH's prestigious Artificial Intelligence Resource, and he serves as an associate editor for top-tier AI in Medical Imaging journals such as IEEE Transactions on Medical Imaging and Elsevier Medical Image Analysis. Dr. Bagci teaches a Medical AI course, and he supervises clinical fellows, postdoc researchers, master's and PhD students, and undergraduate students, as well as high school students. 
Dr. Bagci's AI Lab has several high-impact research projects, most of them are funded by NIH. Some of the research topics are as follows:
  1. Hybrid Intelligence: Human in the loop and Eye Tracking
  2. AI in Pancreatic Diseases (Cancer, Cysts, Diabetes, and Pancreatitis)
  3. AI in Liver Health (HCC and Cirrhosis, Interventional Therapy)
  4. AI for Thoracic Applications (Lung Cancer, PASC Fibrosis, etc).
  5. HITPIRADS: AI for Prostate Cancer Management
  6. FIDELIS: AI for Radiation Oncology Applications
  7. Federated Learning for Healthcare Applications
  8. Explainable/Interpretable AI for High Risk Applications
  9. AI for Cardiology Applications
For further details on The Machine and Hybrid Intelligence Lab, visit www.bagcilab.com
For more information on my research, please view my Feinberg School of Medicine faculty profile.

Profile, Grants, & Publications

View my profile, grants, & publications on Northwestern Scholars.

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