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27. Histogram of Cell Types: Deep Learning for Automated Bone Marrow Cytology

Author

Tayebi, Rohollah Moosavi, Mu, Youqing, Dehkharghanian, Taher, Ross, Catherine, Sur, Monalisa, Foley, Ronan, Tizhoosh, Hamid R., and Campbell, Clinton JV

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Bone marrow cytology is required to make a hematological diagnosis, influencing critical clinical decision points in hematology. However, bone marrow cytology is tedious, limited to experienced reference centers and associated with high inter-observer variability. This may lead to a delayed or incorrect diagnosis, leaving an unmet need for innovative supporting technologies. We have developed the first ever end-to-end deep learning-based technology for automated bone marrow cytology. Starting with a bone marrow aspirate digital whole slide image, our technology rapidly and automatically detects suitable regions for cytology, and subsequently identifies and classifies all bone marrow cells in each region. This collective cytomorphological information is captured in a novel representation called Histogram of Cell Types (HCT) quantifying bone marrow cell class probability distribution and acting as a cytological "patient fingerprint". The approach achieves high accuracy in region detection (0.97 accuracy and 0.99 ROC AUC), and cell detection and cell classification (0.75 mAP, 0.78 F1-score, Log-average miss rate of 0.31). HCT has potential to revolutionize hematopathology diagnostic workflows, leading to more cost-effective, accurate diagnosis and opening the door to precision medicine.

Published
2021

44. Smart Wireless Communication is the Cornerstone of Smart Infrastructures

Author

Weitnauer, Mary Ann, Rexford, Jennifer, Laneman, Nicholas, Bloch, Matthieu, Griljava, Santiago, Ross, Catherine, and Chang, Gee-Kung

Subjects
Computer Science - Computers and Society
Abstract

Emerging smart infrastructures, such as Smart City, Smart Grid, Smart Health, and Smart Transportation, need smart wireless connectivity. However, the requirements of these smart infrastructures cannot be met with today's wireless networks. A new wireless infrastructure is needed to meet unprecedented needs in terms of agility, reliability, security, scalability, and partnerships. We are at the beginning of a revolution in how we live with technology, resulting from a convergence of machine learning (ML), the Internet-of-Things (IoT), and robotics. A smart infrastructure monitors and processes a vast amount of data, collected from a dense and wide distribution of heterogeneous sensors (e.g., the IoT), as well as from web applications like social media. In real time, using machine learning, patterns and relationships in the data over space, time, and application can be detected and predictions can be made; on the basis of these, resources can be managed, decisions can be made, and devices can be actuated to optimize metrics, such as cost, health, safety, and convenience., Comment: A Computing Community Consortium (CCC) white paper, 5 pages

Published
2017

46. High-Level Disinfection (HLD): A Guide to Implementing a Collaborative and Comprehensive Ultrasound Probe Disinfection Program: Part IV of IV: How to Maintain Compliance and What to Look Out for.

Author

Hoffman, Katharine J., Rice, Cortní, Waldowski, Lisa, Johnson, Raymond, Saltarelli, Nicholas, Taite, Lisa, Ross, Catherine, Williams, Randi, Huyen Le, Allison Nelson, C., Norris, Angela, Ray, Leslie, Coleman, Robert, Boiles, Gina, Dean, Nikki, King, Joslynn, Winter, Cindy, and Stephenson, G. Robert

Subjects
EXECUTIVES, DIAGNOSTIC ultrasonic imaging, HEALTH facilities, ULTRASONIC imaging, MEDICAL personnel, MULTIPURPOSE buildings
Published
2024

48. Trends in Disease Severity Among Critically Ill Children With Severe Acute Respiratory Syndrome Coronavirus 2: A Retrospective Multicenter Cohort Study in the United States*

Author

Ross, Catherine E., Burns, Jeffrey P., Grossestreuer, Anne V., Bhattarai, Pallav, McKiernan, Christine A., Franks, Jennifer D., Lehmann, Sonja, Sorcher, Jill L., Sharron, Matthew P., Wai, Kitman, Al-Wahab, Haitham, Boukas, Konstantinos, Hall, Mark W., Ru, George, Sen, Anita I., Rajasekhar, Hariprem R., Kleinman, Lawrence C., McGuire, John K., Arrington, Amy S., Munoz-Rivas, Flor, Osborne, Christopher M., and Shekerdemian, Lara S.

Published
2023
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