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GTC ON-DEMAND

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Abstract:

We'll present a deep learning-based analysis framework for making key decisions about heart valve replacement and valve design. We'll describe how we use deep learning to predict valve performance measures, which makes these measurements accessible to physicians who lack expert computational knowledge. We will explain how our trained DL framework can be used interactively to predict valve-performance measures with the same fidelity as time-consuming biomechanics simulations. We'll also discuss how our tool can help doctors with heart valve diagnosis, ultimately improving patient care.

We'll present a deep learning-based analysis framework for making key decisions about heart valve replacement and valve design. We'll describe how we use deep learning to predict valve performance measures, which makes these measurements accessible to physicians who lack expert computational knowledge. We will explain how our trained DL framework can be used interactively to predict valve-performance measures with the same fidelity as time-consuming biomechanics simulations. We'll also discuss how our tool can help doctors with heart valve diagnosis, ultimately improving patient care.

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Topics:
Medical Imaging & Radiology, AI & Deep Learning Research, Consumer Engagement & Personalization
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9455
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Abstract:
We'll present a GPU-accelerated deep-learning framework for cyber-manufacturing, which enables real-time feedback to designers regarding the manufacturability of a computer-aided design model. We'll talk about a 3D-convolutional neural network-based approach for learning the manufacturability of a mechanical component. The 3D-CNN can recognize the features in a CAD model and classify it to be manufacturable or non-manufacturable with a greater accuracy than traditional rule-based methods. We'll discuss a novel GPU-accelerated voxelization algorithm used to discretize the CAD model and prepare it for deep learning. We'll briefly outline the challenges in training a 3D-CNN using complex CAD models on a GPU (NVIDIA TITAN X) with limited memory. Finally, we'll touch upon different methods to extend the framework to other manufacturing processes, such as additive manufacturing and milling.
We'll present a GPU-accelerated deep-learning framework for cyber-manufacturing, which enables real-time feedback to designers regarding the manufacturability of a computer-aided design model. We'll talk about a 3D-convolutional neural network-based approach for learning the manufacturability of a mechanical component. The 3D-CNN can recognize the features in a CAD model and classify it to be manufacturable or non-manufacturable with a greater accuracy than traditional rule-based methods. We'll discuss a novel GPU-accelerated voxelization algorithm used to discretize the CAD model and prepare it for deep learning. We'll briefly outline the challenges in training a 3D-CNN using complex CAD models on a GPU (NVIDIA TITAN X) with limited memory. Finally, we'll touch upon different methods to extend the framework to other manufacturing processes, such as additive manufacturing and milling.  Back
 
Topics:
Intelligent Machines, IoT & Robotics, Artificial Intelligence and Deep Learning, Computational Fluid Dynamics, Computer Aided Engineering, AEC & Manufacturing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7397
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