Category: DataS

Machine Learning Reduces Uncertainty in Breast Cancer Diagnoses

Researchers at Michigan Tech have developed a machine learning model for detecting breast cancer from histopathology images — tissues and cells examined under microscope. The model can classify benign and malignant tumors from these images — and it can also evaluate the uncertainty in its predictions.

In their paper recently published in the journal IEEE Transactions on Medical Imaging, graduate students Ponkrshnan Thiagarajan and Pushkar Khairnar and Susanta Ghosh (ME-EM) outline their novel probabilistic machine learning model, which outperforms similar models.

Read the full story at mtu.edu/news.

Thomas Oommen is PI on $188K DoT Project

Thomas Oommen (GMES/MTTI/ICC-DataS) is the principal investigator (PI) on a project that has received a $188,433 research and development contract from the U.S. Department of Transportation, Federal Railroad Administration.

The project is titled “An Integrated Automated Decision Support System for Ground Hazard Risk Mitigation for Railway using Remote Sensing and Traditional Condition Monitoring Data.”

Pasi Lautala and Melanie Kueber Watkins (CEE/MTTI) and Colin Brooks (MTRI/MTTI) are co-PIs on this potential three-year project.

Thomas Oommen, Jeremy Bos Are Co-PIs on $398.8K U.S. Army Project

Ryan Williams (GLRC) is the PI on a project that has received a $398,843 research and development grant from the U.S. Army Construction Engineering Research Laboratory.

The project is titled “Robotic Platform Soil and Terrain Characterization for Close to Real Time GO/NOGO Maps.”

Thomas Oommen (GMES/GLRC/ICC-DataS) and Jeremy Bos (ECE/GLRC/ICC-DataS) are co-PIs on this potential two-year project.

Snehamoy Chatterjee Awarded R and D Contract from US DHHS

Dr. Snehamoy Chatterjee (GMES/ICC-DataS) is the principal investigator (PI) on a project that has received a $288,343 research and development contract from the Department of Health and Human Services, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health (NIOSH).

The project is titled “Mine Health and Safety Big Data Analysis and Text Mining by Machine Learning Algorithms.”

Aref Majdara (ECE/ICC) is a co-PI on this potential two-year project.