If you’re interested in the future of healthcare, you won’t want to miss the GPU Technology Conference, where healthcare innovators will show how they’re using AI and GPUs to revolutionize medicine and improve patient care.
You’ll hear from luminaries at institutions like the American College of Radiology, Siemen Healthineers and Johns Hopkins University at GTC, taking place March 26-29 in San Jose.
And you’ll have the opportunity to discover how deep learning is advancing medical imaging, precision medicine, real-time diagnosis and more.
Here are some of the top healthcare speakers and sessions at GTC:
From Challenges to Impact of Machine Learning in Clinical Practice – American College of Radiology (ACR) and the Medical Image Computing and Computer Assisted Interventions (MICCAI) Society
In this workshop, experts will discuss how deep learning can be used to diagnose disease and predict a patient’s prognosis, as well as to plan and monitor therapy. The event will also launch a drive to overcome the obstacles involved in bringing algorithms developed in a research environment into clinical practice. Speakers include:
- Keith Dreyer, chief science officer, ACR Data Science Institute – Harnessing AI: Creating a healthcare AI ecosystem
- Alejandro Frangi, MICCAI 2018 general chair – Accelerating medical device development in medical imaging
- Mike Tilkin, executive vice president and chief information officer, ACR – Workflow and regulatory challenges to algorithm implementation
- Wiro Niessen, MICCAI society president – From promising algorithms to clinical practice: next generation of challenges.
Keith Bigelow and Erik Steen, GE Healthcare – GE’s Evolution from HPC to AI in Healthcare
Keith Bigelow, general manager of analytics at GE Healthcare (GEHC), and Erik Steen, a chief engineer with GE Vingmed Ultrasound, will talk about deep learning at GE’s healthcare business and how they’re accelerating algorithm development by building a platform on top of the NVIDIA GPU Cloud. They’ll also discuss the evolution of GEHC’s cardiovascular ultrasound scanner and explain how they’re integrating deep learning inference into GEHC’s imaging system.
Mark DePristo, Google Inc. – An Introduction to Deep Learning in Genomics and Its Application to Genome Sequencing
Mark DePristo leads Google Brain’s team, which is applying deep learning in TensorFlow to problems in genetics and genomics. He’ll highlight deep learning applications for biomedical problems and do a deep dive into three recent examples from Google Brain.
Thomas Fuchs, Memorial Sloan Kettering Cancer Center – Computational Pathology at Scale: Changing Healthcare Practice One PetaByte at a Time
Thomas Fuchs, an associate professor and director of computational pathology at Memorial Sloan Kettering Cancer Center, will talk about how deep learning is transforming pathology and, particularly, how he’s building clinical-grade AI at the center. He’ll explain how he’s advancing deep learning for tumor detection and segmentation by training high-capacity models and implementing the resulting systems in the clinic.
Dan Golden is director of machine learning at medical-imaging startup Arterys, which recently received its first FDA clearance for its Oncology AI suite. Golden will discuss radiological diagnosis and interpretation, and explain Arterys’ work to give radiologists a lung nodule reference library that automatically retrieves historical cases relevant to the current case.
Mei Han, Ping An Technology – R&D on Medical Imaging
Mei Han, who directs Ping An Technology’s U.S. lab, will talk about how her company is using deep learning to advance medical imaging. She’ll discuss technical details of her team’s deep learning approaches and talk about Ping An’s medical research direction and scope.
Avanti Shrikumar, Stanford University – Not Just a Black Box: Interpretable Deep Learning for Genomics and Beyond
Avanti Shrinkumar, a Ph.D. candidate in computer science at Stanford, will discuss her work on interpretable algorithms — algorithms that provide detailed explanations for predictions made by a deep learning model and discover recurring patterns across a dataset. Using examples from genomics, she’ll show how combining deep learning with her interpretability algorithms leads to biological insights.