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Deep Learning with Big Data in Medical Imaging

Our lab is broadly interested in applying cutting edge techniques in machine learning, particularly deep learning, to a variety of problems in medical imaging. We work with a varity of imaging domains, including radiology,, pathology, and ophthalmology. The goal in applying these methods to large numbers of images is to automatically detect abnormalities, to segment them, and to identify phenotypes in the images that can be used for automatic disease classification and to enable "precision medicine." We have applied deep learning methods in the following ways to date:

  • Radiology: automated detection of abnormalities, such as masses on mammography; automated classification of lesions, and automated segmentation of lesions
  • Pathology: automated classification of the type of a patient's cancer
  • Ophthalmology: automated classification of the type and severity of eye disease in diabetic retinopathy

We have a number of publications on these topics in the Publications area

Specific recent accomplishments:

  • Lesion detection
  • Lesion segmentation
  • Disease classification
  • Visualization of what deep networks learn
  • Toolkit for deep learning