Mr. Adam M. Saunders Profile

Adam M. Saunders

SPIE Involvement:

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Area of Expertise:

Medical imaging , Deep learning

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Personal Website

I am a PhD student in electrical and computer engineering at Vanderbilt University. Before Vanderbilt, I earned a BEE degree from University of Dayton in 2023. My current research interests include deep learning applications in medical imaging and quantitative imaging methods for MRI.

SPIE Journal Paper | 14 November 2024 Open Access

Super-resolution multi-contrast unbiased eye atlases with deep probabilistic refinement

Ho Hin Lee, Adam Saunders, Michael Kim, Samuel Remedios, Lucas Remedios, Yucheng Tang, Qi Yang, Xin Yu, Shunxing Bao, Chloe Cho, Louise Mawn, Tonia Rex, Kevin Schey, Blake Dewey, Jeffery Spraggins, Jerry Prince, Yuankai Huo, Bennett Landman

JMI, Vol. 11, Issue 06, 064004, (November 2024) https://doi.org/10.1117/12.10.1117/1.JMI.11.6.064004

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SPIE Journal Paper | 5 November 2024 Open Access

Data-driven nucleus subclassification on colon hematoxylin and eosin using style-transferred digital pathology

Lucas Remedios, Shunxing Bao, Samuel Remedios, Ho Hin Lee, Leon Cai, Thomas Li, Ruining Deng, Nancy Newlin, Adam Saunders, Can Cui, Jia Li, Qi Liu, Ken S. Lau, Joseph Roland, Mary Washington, Lori Coburn, Keith Wilson, Yuankai Huo, Bennett Landman

JMI, Vol. 11, Issue 06, 067501, (November 2024) https://doi.org/10.1117/12.10.1117/1.JMI.11.6.067501

KEYWORDS: Education and training, Tissues, Colon, Data modeling, Diseases and disorders, Virtual colonoscopy, Image segmentation, Performance modeling, Pathology, Cross validation

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PurposeCells are building blocks for human physiology; consequently, understanding the way cells communicate, co-locate, and interrelate is essential to furthering our understanding of how the body functions in both health and disease. Hematoxylin and eosin (H&E) is the standard stain used in histological analysis of tissues in both clinical and research settings. Although H&E is ubiquitous and reveals tissue microanatomy, the classification and mapping of cell subtypes often require the use of specialized stains. The recent CoNIC Challenge focused on artificial intelligence classification of six types of cells on colon H&E but was unable to classify epithelial subtypes (progenitor, enteroendocrine, goblet), lymphocyte subtypes (B, helper T, cytotoxic T), and connective subtypes (fibroblasts). We propose to use inter-modality learning to label previously un-labelable cell types on H&E.ApproachWe took advantage of the cell classification information inherent in multiplexed immunofluorescence (MxIF) histology to create cell-level annotations for 14 subclasses. Then, we performed style transfer on the MxIF to synthesize realistic virtual H&E. We assessed the efficacy of a supervised learning scheme using the virtual H&E and 14 subclass labels. We evaluated our model on virtual H&E and real H&E.ResultsOn virtual H&E, we were able to classify helper T cells and epithelial progenitors with positive predictive values of 0.34±0.15 (prevalence 0.03±0.01) and 0.47±0.1 (prevalence 0.07±0.02), respectively, when using ground truth centroid information. On real H&E, we needed to compute bounded metrics instead of direct metrics because our fine-grained virtual H&E predicted classes had to be matched to the closest available parent classes in the coarser labels from the real H&E dataset. For the real H&E, we could classify bounded metrics for the helper T cells and epithelial progenitors with upper bound positive predictive values of 0.43±0.03 (parent class prevalence 0.21) and 0.94±0.02 (parent class prevalence 0.49) when using ground truth centroid information.ConclusionsThis is the first work to provide cell type classification for helper T and epithelial progenitor nuclei on H&E.

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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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