Dr. Aristeides Tsaris Profile

Dr. Aristeides Tsaris

at Oak Ridge National Lab

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Publications (2)

Proceedings Article | 7 April 2023 Poster + Paper

A comparison of histopathology imaging comprehension algorithms based on multiple instance learning

Adam Saunders, Sajal Dash, Aristeidis Tsaris, Hong-Jun Yoon

Proceedings Volume 12471, 124711M (2023) https://doi.org/10.1117/12.2654265

KEYWORDS: Education and training, Transformers, Data modeling, Machine learning, Tumor growth modeling, Tissues, Deep learning, Cancer, Biological samples, Evolutionary algorithms

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Proceedings Article | 4 April 2022 Poster + Presentation + Paper

Image transformers for classifying acute lymphoblastic leukemia

Priscilla Cho, Sajal Dash, Aristeides Tsaris, Hong-Jun Yoon

Proceedings Volume 12033, 120332F (2022) https://doi.org/10.1117/12.2611496

KEYWORDS: Transformers, Blood, Leukemia, Cancer, Visual process modeling, Bone, Statistical modeling, Data modeling, Tumor growth modeling, Machine learning

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Cancer is the leading cause of death by disease in American children. Each year, nearly 16,000 children in the United States and over 300,000 children globally are diagnosed with cancer. Leukemia is a form of blood cancer that originates in the bone marrow and accounts for one-third of pediatric cancers. This disease occurs when the bone marrow contains 20% or more immature white blood cell blasts. Acute lymphoblastic leukemia is the most prevalent leukemia type found in children, with half of all annual cases in the U.S. diagnosed for subjects under 20 years of age. To diagnose acute lymphoblastic leukemia, pathologists often conduct a morphological bone marrow assessment. This assessment determines whether the immature white blood cell blasts in bone marrow display the correct morphological characteristics, such as size and appearance of nuclei. Pathologists also use immunophenotyping via multi-channel flow cytometry to test whether certain antigens are present on the surface of blast cells; the antigens are used to identify the cell lineage of acute lymphoblastic leukemia. 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. Training a reliable classification model to distinguish between mature and immature white blood cells is essential to the decision support system. Here, 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. Additionally, the latent self-attention architecture provided attention maps for a given image, providing clues as to which portion(s) of the image were significant in decision-making. We applied the Vision Transformer model and a convolutional neural network model to an acute lymphoblastic leukemia classification dataset of 12,528 samples and achieved accuracies of 88.4% and 86.2%.

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