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Machine Learning

Case Western Reserve University researchers and partners, including a collaborator at Cleveland Clinic, are pushing the boundaries of how "smart" diagnostic-imaging machines identify cancers--and uncovering clues outside the tumor to tell whether a patient will respond well to chemotherapy.

With Skin Cancer Awareness Month upon us, Colorado State University researcher Jesse Wilson is accelerating research to improve imaging and detection of melanoma, the most deadly form of skin cancer, and the fifth most common cancer in the United States.

Wilson, an associate professor in the Department of Electrical and Computer Engineering (ECE) and in the School of Biomedical Engineering (SBME), is one of 15 researchers selected for a Young Investigator Award from the Melanoma Research Alliance.

Please give an overview of the past research into machine learning and artificial intelligence in medical imaging. What are we currently able to do with this research?

The two major tasks in medical imaging that appear to be naturally predestined to be solved with AI algorithms are segmentation and classification. Most of techniques used in medical imaging were conventional image processing, or more widely formulated computer vision algorithms.

 

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