To check the pulse on how AI is transforming the healthcare industry, look no further than the eighth annual GPU Technology Conference, next week at the San Jose Convention Center.
GTC’s healthcare track will offer 50-plus sessions detailing the latest thinking on how AI and deep learning are empowering physicians to do more in such areas as radiology, pathology, genomics and drug discovery.
Using GPUs, researchers are developing algorithms to analyze data faster and more accurately, training computers to help make new discoveries and guiding targeted treatments. This lets physicians seek new ways to treat aggressive diseases, such as cancer, with lower error rates.
The Doctor Will See You Now, at GTC
Among the talks at GTC, researchers at Stanford will share how they trained a neural network to recognize skin cancer lesions at the accuracy level of an experienced dermatologist. Pathologists at the university applied the same deep learning technology to train a computer on liver lesion detection, helping augment the work of radiologists in their daily practice.
Deep learning is also driving new discoveries, and expanding the reach of GPU computing in medical communities. Medical researchers at the Mayo Clinic used it to identify the genomic information of a brain tumor, without even performing a biopsy.
In addition to talks, six hands-on workshops will cover radiomics, image segmentation and quantitative imaging.
Among sessions not to miss:
- Stanford University’s Curt Langlotz on how his team aims to dramatically reduce diagnostic imaging errors by creating a massive clinical imaging research resource, and linking it to genomic data, tissue banks and information from medical records.
- GE Healthcare’s Michael Dahlweid on how deep learning in healthcare can be used beyond medical imaging and help overcome challenges faced by care-givers.
- Arterys’ Daniel Golden on how cloud computing and deep learning in a clinical setting can help reduce errors in cardiac radiology.
- Memorial Sloan Kettering Cancer Center’s Thomas Fuchs on how they are building a computational pathology AI based on hundreds of NVIDIA GPUs and a petabyte of clinical data to change the future of medical diagnosis and research.
- Stanford’s Olivier Gevaert, who’ll demonstrate a deep learning framework to predict survival of lung cancer patients.
Join these sessions and more from physicians, scientists, researchers, and academic and commercial institutes working to ensure everyone can benefit from early disease detection, higher quality diagnosis and more choice when it comes to care.