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March 3, 2022

Oncological Applications of Deep Learning Generative Adversarial Networks

Author Affiliations
  • 1Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
  • 2Internal Medicine B, Assuta Medical Center, Ashdod, Israel
  • 3Faculty of Health Sciences, Ben-Gurion University of the Negev, Be’er Sheva, Israel
  • 4Institute for Healthcare Delivery Science, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York
JAMA Oncol. 2022;8(5):677-678. doi:10.1001/jamaoncol.2021.8202

In the past decade, deep learning has revolutionized machine problem solving. Advances in computing power and data access have led this technology to grow from distinguishing between cats and dogs to playing Go at a superhuman level. Medical applications were enabled by AlexNet, a convolutional neural network that provided breakthroughs allowing for computer vision tasks, such as image labeling, identification, or segmentation. These advances are responsible for new clinical use cases, such as classifying computed tomography tumor images as benign or malignant. However promising these new advances may appear, major challenges exist in acquiring data sets large enough to be clinically relevant.

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