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Conference 12033 > Paper 12033-112
Paper 12033-112

Image transformers for classifying acute lymphoblastic leukemia

In person: 23 February 2022 • 5:30 PM - 7:00 PM PST

Abstract

Leukemia is a form of blood cancer that originates in the bone marrow and accounts for one-third of pediatric cancers. Acute lymphoblastic leukemia is the most prevalent leukemia type found in children. To diagnose acute lymphoblastic leukemia, pathologists often conduct a morphological bone marrow assessment. These manual processes require well-trained personnel and medical professionals, thus being costly in time and expenses. Computerized decision support via machine learning can accelerate the diagnosis process and reduce the cost. We adopted the Vision Transformer model to classify white blood cells. The Vision Transformer achieved superb classification performance compared to state-of-the-art convolutional neural networks while requiring less computational resources for training. We applied the Vision Transformer model to an acute lymphoblastic leukemia classification dataset of 12,528 samples and achieved an accuracy of 88.4%.

Presenter

Priscilla Cho
Emory Univ. (United States)
Ms. Priscilla Cho is an undergraduate student at Emory University majoring in Chemistry.
Presenter/Author
Priscilla Cho
Emory Univ. (United States)
Author
Oak Ridge National Lab. (United States)
Author
Oak Ridge National Lab. (United States)
Author
Oak Ridge National Lab. (United States)