Amazon is not just about Echo products, cloud services and e-commerce platforms. It has actively been contributing in science and development as well. And the most recent advancement in the field comes in the form of Amazon Web Services (AWS) and Pittsburgh Health Data Alliance (PHDA) collaborating to produce more accurate machine learning models for breast cancer screening and depression.
In the blog post, it has been mentioned how researchers from the University of Pittsburgh Medical Center the University of Pittsburgh, and Carnegie Mellon University who were already supported by the PHDA, received additional support from Amazon Research Awards to use machine learning techniques to study breast cancer risk, identify depression markers, and understand what drives tumor growth, among other projects.
As a part of this collaboration, a research team led by Shandong Wu, an associate professor in the University of Pittsburgh Department of Radiology, is using deep-learning systems to analyze mammograms in order to predict the short‐term risk of developing breast cancer. Also mentioned is that the team of experts in computer vision, deep learning, bioinformatics, and breast cancer imaging are working together to develop a personalized approach for patients undergoing breast cancer screening.
“This preliminary work demonstrates the feasibility and promise of applying deep-learning methodologies for in-depth interpretation of mammogram images to enhance breast cancer risk assessment,” Wu said. “Identifying additional risk factors for breast cancer, including those that can lead to a more personalized approach to screening, may help patients and providers take more appropriate preventive measures to reduce the likelihood of developing the disease or catching it early on when interventions are most effective.”
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Also mentioned is the second project wherein Louis-Philippe Morency, associate professor of computer science at CMU, and Eva Szigethy, a clinical researcher at UPMC are developing sensing technologies that can automatically measure subtle changes in individuals’ behavior — such as facial expressions and use of language — that can act as biomarkers for depression.
This involves machine learning and heavy computational load. However, running experiments in parallel on multiple GPUS AWS services allowed the researchers to train their models in a few days instead of weeks.
Source: HD Tech
