Computer Vision News Computer Vision News 32 Auto3DSeg, which is one of these AutoML techniques whereby specifying simply two YAML files or description files, you could specify where all your data was located within one of the files. And then the other files and really just 5-10 lines give a description of the problem that you were trying to solve. The fact that it was MR data versus CT data. It was a segmentation task versus a classification task. So really high-level description and then a description of the data and the Auto3DSeg AutoML technique would go out, evaluate three different neural networks, and do five fold cross validation on it. Look at your data. Look at the options for neural network models that were available. Train the systems up and then pick the best ensemble from the available networks that it evaluated as the final solution for your system. With these two simple configuration files, all this stuff is going on in the background. Yet, that combination of material in the background resulted in winning entries and four MICCAI grand challenges last year. For me, that was a huge success. That was AI enabling AI. And I think we'll see more and more of that. Can any of this be exported outside of the medical environment? That makes perfect sense. And that is the one of the wonderful things in this community aspect of MONAI. We already see people using MONAI outside of the medical field for traditional computer vision tasks. So yes, very much so. In my old job, one of the early adopters of MONAI was our computer vision teams. As you start looking at point clouds and other volumetric data in particular that exists within these other fields, MONAI has some wonderful techniques for dealing with volumetric data. 3D convolution networks, convolution techniques, sliding window approaches for massive images, pathology images and so forth that really help solve problems as images get larger and other fields evolve. MONAI is going to be a great go-to tool for them! Can you help out the autonomous vehicle guys? It's taking some time for them to get their show going. Yeah, and it goes both ways. I mean, we've learned so much from them and there's a lot of positive synergies. We're starting to see the medical field being represented at CVPR and traditional computer vision conferences because they're recognizing that we have something to contribute back. As a commercial company, you’re investing a lot of resources here. Where will the money come back from? MONAI - Medical Open Network for AI
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