December 3, 2019

236 words 2 mins read

Smart diagnosis in healthcare with deep learning

Smart diagnosis in healthcare with deep learning

Deep learning with ConvNet in particular has emerged as a promising tool in medical research labs and diagnostic centers to help analyze images and scans, and systems are now surpassing human capability for manual inspection. Nishant Sahay explains how to apply deep learning to analyze high-end microscope images and X-ray scans to provide accurate diagnosis.
Talk Title Smart diagnosis in healthcare with deep learning
Speakers Nishant Sahay (Wipro Limited)
Conference Artificial Intelligence Conference
Conf Tag Put AI to Work
Location Beijing, China
Date April 11-13, 2018
URL Talk Page
Slides Talk Slides
Video

Artificial intelligence, and specifically deep learning, has begun to be used at the forefront of research across a variety of domains, including healthcare. One of the areas in healthcare where deep learning can have a great impact is learning from past data to diagnose disease. Biophotonics and radiology in particular provide a large dataset of archived images that can be used in computer-aided deep learning systems to classify and diagnose health issues in patients. Deep learning, computer vision, and big data tools make it possible to automate much of the manual inspection and complex workflow in radiology and biophotonics. Nishant Sahay explains how to apply deep learning to analyze high-end microscope images and X-ray scans to provide accurate diagnosis, exploring examples of how to automatically detect anomalies from samples of digital images using a combination of deep learning techniques, OpenCV, and Apache Spark.

intelligence healthcare health intel apache dataset spark big data deep learning computer vision opencv artificial intelligence
comments powered by Disqus
Modernizing the healthcare industry with AI

Modernizing the healthcare industry with AI

December 3, 2019

Artificial intelligence is transforming every industry, but the role it will play in healthcare is profound. Arjun Bansal explains how AI can give physicians new insights and speed time to diagnosis by leveraging vast amounts of healthcare data and how it can reduce the time and money spent to develop new medicines.
Accelerating deep learning on Apache Spark using BigDL with coarse-grained scheduling

Accelerating deep learning on Apache Spark using BigDL with coarse-grained scheduling

November 30, 2019

The BigDL framework scales deep learning for large datasets using Apache Spark. However there is significant scheduling overhead from Spark when running BigDL at large scale. Shivaram Venkataraman and Sergey Ermolin outline a new parameter manager implementation that along with coarse-grained scheduling can provide significant speedups for deep learning models like Inception and VGG.
Building deep reinforcement learning applications on BigDL and Spark

Building deep reinforcement learning applications on BigDL and Spark

December 3, 2019

Deep reinforcement learning is a thriving area and has wide applications in industry. Arsenii Mustafin shares his experience developing deep reinforcement learning applications on BigDL and Spark.
Automatic 3D MRI knee damage classification with 3D CNN using BigDL on Spark

Automatic 3D MRI knee damage classification with 3D CNN using BigDL on Spark

November 29, 2019

Damage to the meniscus is a physically limiting injury that can lead to further medical complications. Automatically classifying this damage at the time of an MRI scan would allow quicker and more accurate diagnosis. Jennie Wang, Valentina Pedoia, Berk Norman, and Yulia Tell offer an overview of their classification system built with 3D convolutional neural networks using BigDL on Apache Spark.
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.
Smart agriculture: Blending IoT sensor data with visual analytics

Smart agriculture: Blending IoT sensor data with visual analytics

November 21, 2019

Mike Prorock offers an overview of mesur.io, a game-changing climate awareness solution that combines smart sensor technology, data transmission, and state-of-the-art visual analytics to transform the agricultural and turf management market. Mesur.io enables growers to monitor areas of concern, providing immediate benefits to crop yield, supply costs, farm labor overhead, and water consumption.