Introduction to Machine & Deep Learning

Frontiers for Deep Learning and Cancer - Eric Stahlberg, Frederick National Laboratory for Cancer Research:

Introduction to Deep Learning - Rick Stevens, Argonne National Laboratory:

Deep Learning: Perspectives from the NIH

Accelerating Image Analysis Workflows Using Deep Learning - Yanling Liu, Frederick National Laboratory for Cancer Research:

The Impact of Deep Learning on Radiology - Ronald Summers, Diagnostic Radiology Department, NIH Clinical Center: 

Applying Deep Learning to Big Data: Perspectives from the Department of Energy with Applications in Health

Cellular Level Deep Learning - Fangfang Xia, Argonne National Laboratory:

Molecular Level Deep Learning - Brian Van Essen, Lawrence Livermore National Laboratory: 

Population Level Deep Learning - Arvind Ramanathan, Oak Ridge National Laboratory: 

Getting Started With CANDLE - Rick Stevens, Argonne National Laboratory:

Overview of CANDLE Layers: Workflows, Scripting, and Parallelization Strategies

Workflows - Justin Wozniak, Argonne National Laboratory:

Scripting - Tom Brettin, Argonne National Laboratory:

Parallelization Strategies - Brian Van Essen, Lawrence Livermore National Laboratory:

Preparing Data for Deep Learning

Arvind Ramanathan, Oak Ridge National Laboratory:

Fangfang Xia, Argonne National Laboratory:

Brian Van Essen, Lawrence Livermore National Laboratory:

Interactive Session with CANDLE Developers

Hyperparameter Optimization and Uncertainty Quantification - Justin Wozniak, Argonne National Laboratory:

Installing CANDLE into Other Environments - George Zaki, Frederick National Laboratory for Cancer Research:

Event Recap - CANDLE Workshop @ NIH, 2/21/18-2/22/18

With over 150 attendees representing 22 NIH institutes, government collaborators, industry, and academia, the February 2018 CANDLE Workshop at NIH proved to be a truly engaging and successful event!

Throughout the two-day workshop, many different perspectives on machine and deep learning were explored along with their applications to cancer research and advancing precision oncology.

The first day included a general overview of machine and deep learning along with applications of deep learning in health from the NIH and Department of Energy perspectives.

The second day included hands-on training of deep learning along with tutorials on preparing data for deep learning and installing the CANDLE deep learning framework into other environments.

Some of the common exploratory areas of interest included imaging analysis, next-generation sequencing, genomics and genetics, text analysis, and big data.  

As the majority of experience using machine and deep learning was limited within the workshop, this event provided the opportunity for participants to progress from a limited understanding of ML/DL technologies, to having the ability to train deep learning models with their own data sets, and on their own computers!

As an outcome of this workshop, future events are already in the works which will aim to address the diverse set of challenges and opportunities raised within the community, and to meet the increasing demands for deep learning technology in present-day research.