Medical imaging technologies will play a key role in the future of medical diagnosis and therapeutics, helping doctors make better medical diagnostics. Machine learning applied to image recognition of organs, even in the presence of disease, can minimize the possibility of medical errors and speed up disease diagnosis.
Mauro Damo and Wei Lin offer an overview of an approach to identify bladder cancer in patients using nonsupervised and supervised machine learning techniques on more than 5,000 magnetic resonance images from the Cancer Imaging Archive. Additionally, the algorithms attempted to identify significant differences between the images to assess what features could be relevant for bladder cancer detection. The resulting model achieves 79.77% accuracy, highlighting the measurable impact deep learning can have in the healthcare industry.
Mauro Damo is a senior data scientist at Dell, where he is responsible for helping organizations identify, develop, and implement analytical solutions in big data environments, focusing on solving business problems. He has developed and implemented analytical projects for a number of companies in a range of industries, including health care, mortgage insurance, financial brokers, cable companies, nongovernmental organizations, and supply chain. He has experience with a wide range of supervised and unsupervised models, including time series, graphs analysis, optimization models, and deep learning models such as convolutional neural networks, recurrent neural networks, neural networks, clustering, dimensional reduction, tree algorithms, frequent pattern mining, ensembles models, Markov chains, and gradient descent. Mauro holds patents, has authored several papers, and speaks at international conferences, seminars, and classes. His main programming languages are Python, R and SQL. He holds an MS in business, an MBA in finance, an undergraduate degree in business, and an associate degree in computer science.
Wei Lin is senior manager at Dell EMC and chief data scientist for the company’s Big Data Practice, where he is responsible for planning the company’s data science strategy and leading data science services delivery as well as leading data scientist project delivery and the hiring, training, and certification of new data scientists. He hosts Dell EMC’s data science mentorship program, which shares data scientists’ engagement findings, industry experience, techniques, and trends. His successes include developing Dell EMC’s data science field consulting methodology, Descriptive, Exploration, Predictive and Prescriptive (DEPP), which provides a practical analytics roadmap and approaches for an organization’s business initiatives and data and analytic requirements. Wei has over 20 years of experience in predictive analytics, including analytical modeling, architecture design, data warehousing, reporting, and marketing. Previously, he was the principal consultant at IBM, PwC, and Cooper & Lybrand. He has authored over 100 papers, and his work has been published or reported on in professional journals as well as Businessweek and Forbes. Wei holds both an MA and a PhD in electrical engineering, specializing in artificial intelligence, from the State University of New York at Binghamton and a BS in electrical engineering from National Taipei Institute of Technology, Taiwan.
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