November 28, 2019

221 words 2 mins read

Bladder cancer diagnosis using deep learning

Bladder cancer diagnosis using deep learning

Image recognition classification of diseases will minimize the possibility of medical mistakes, improve patient treatment, and speed up patient 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.
Talk Title Bladder cancer diagnosis using deep learning
Speakers Mauro Damo (Dell EMC), Wei Lin (Dell EMC)
Conference Strata Data Conference
Conf Tag Big Data Expo
Location San Jose, California
Date March 6-8, 2018
URL Talk Page
Slides Talk Slides
Video

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.

healthcare health algorithm supervised deep learning machine learning
comments powered by Disqus
Human in the loop: Bayesian rules enabling explainable AI

Human in the loop: Bayesian rules enabling explainable AI

November 25, 2019

Pramit Choudhary explores the usefulness of a generative approach that applies Bayesian inference to generate human-interpretable decision sets in the form of "if. . .and else" statements. These human interpretable decision lists with high posterior probabilities might be the right way to balance between model interpretability, performance, and computation.
Improving user-merchant propensity modeling using neural collaborative filtering and wide and deep models on Spark BigDL at scale

Improving user-merchant propensity modeling using neural collaborative filtering and wide and deep models on Spark BigDL at scale

November 24, 2019

Sergey Ermolin and Suqiang Song demonstrate how to use Spark BigDL wide and deep and neural collaborative filtering (NCF) algorithms to predict a users probability of shopping at a particular offer merchant during a campaign period. Along the way, they compare the deep learning results with those obtained by MLlibs alternating least squares (ALS) approach.
Building career advisory tools for the tech sector using machine learning

Building career advisory tools for the tech sector using machine learning

November 28, 2019

Dice.com recently released several free career advisory tools for technology professionals, including a salary predictor, a tool that recommends the next skills to learn, and a career path explorer. Simon Hughes and Yuri Bykov offer an overview of the machine learning algorithms behind these tools and the technologies used to build, deploy, and monitor these solutions in production.
Classifying job execution using deep learning

Classifying job execution using deep learning

November 28, 2019

Ash Munshi shares techniques for labeling big data apps using runtime measurements of CPU, memory, I/O, and network and details a deep neural network to help operators understand the types of apps running on the cluster and better predict runtimes, tune resource utilization, and increase efficiency. These methods are new and are the first approach to classify multivariate time series.
Continuous machine learning over streaming data

Continuous machine learning over streaming data

November 28, 2019

Roger Barga, Nina Mishra, Sudipto Guha, and Ryan Nienhuis detail continuous machine learning algorithms that discover useful information in streaming data. They focus on explainable machine learning, including anomaly detection with attribution, the ability to reduce false positives through user feedback, and the detection of anomalies in directed graphs.
Deep learning for domain-specific entity extraction from unstructured text

Deep learning for domain-specific entity extraction from unstructured text

November 27, 2019

Mohamed AbdelHady and Zoran Dzunic demonstrate how to build a domain-specific entity extraction system from unstructured text using deep learning. In the model, domain-specific word embedding vectors are trained on a Spark cluster using millions of PubMed abstracts and then used as features to train a LSTM recurrent neural network for entity extraction.