cancer, adding to an already unmanageable workload for radiologists. Further, very small pulmonary nodes are difficult to spot with the human eye. Powered by NVIDIA GPUs on the NVIDIA Clara platform, 12 Sigma Technologies’ σ-Discover/Lung system automatically detects lung nodules as small as .01% of an image, analyzes malignancy with >90% accuracy and provides a decision support tool to radiologists. When optimized on an NVIDIA T4 cluster the system runs up to 18x faster. §V'
> ;wp0äÀÙæÀc HPC > 1&?«ã»âÁÜ > E¥ÝÇã.K>I 90% Prediction Accuracy Publish in Nature April 2019 Tensor Cores Achieved 1.13 EF 2018 Gordon Bell Winner Orders Of Magnitude Speedup 3M New Compounds In 1 Day Time-to-solution Reduced From Weeks To 2 Hours
Caffe 29 hours Facebook Tesla P100 x256 Caffe2 1 hour Google TPUv2 x256 TensorFlow 30 mins PFN Tesla P100 x1024 Chainer 15 mins Tencent Tesla P40 x2048 TensorFlow 6.6 mins SONY Tesla V100 x2176 NNL 3.7 mins Google TPUv3 x1024 TensorFlow 2.2 mins Tesla V100 x2048 MxNet 75 sec ImageNet + ResNet50
applied across all slices, the user can interactively correct the segmentation extreme points. • Interactive Annotation: Allows 6-click organ-annotation. User can apply Auto-segmentation and then correct the extreme point in interactive mode • Annotation and Segmentation models continuously learning from user inputs. AI-ASSISTED ANNOTATION APIs to plug into any existing medical viewer 10X
source data • Optimized pre-trained models with state-of-the art augmentation and data transforms • Get started quickly with Kubeflow training pipelines created by an NVIDIA data scientist TRANSFER LEARNING Tool to create a new network from an existing DNN