How to make sure AI and ML models survive and thrive
SAS' Véronique Van Vlasselaer reveals why managing model performance is as important as putting them into production.
Artificial Intelligence
6 questions about the future of AI
What does the AI enterprise of the future look like? That’s a tough question that I’ve been asked to consider, along with a distinguished panel at Valley ML AI Expo 2020. The title of the panel is, “Life, the Universe and the AI Enterprise of the Future.” Based on an initial chat with panel chair Gautam Khera, I’ve written up some possible topics we’ll be covering on the panel. Consider
Maximize model performance without maximizing effort
Through hyperparameter autotuning, you can maximize a model's performance without maximizing effort. While SAS searches the hyperparameter space in the background, you are free to pursue other work.
Getting started with deep learning using the SAS Language
Deep learning is an area of machine learning that has become ubiquitous with artificial intelligence. The complex, brain-like structure of deep learning models is used to find intricate patterns in large volumes of data. These models have heavily improved the performance of general supervised models, time series, speech recognition, object
Case study: Computer Vision for monitoring tumors using image segmentation
Computer vision can augment radiologists and make the image interpretation process cheaper, faster and more accurate. The ultimate goal is to achieve a better patient outcome facilitated by the use of computer vision.
Convolutional Neural Networks: Briefly
Neural networks, particularly convolutional neural networks, have become more and more popular in the field of computer vision. What are convolutional neural networks and what are they used for?
Demo of deep learning to detect tumors in liver CT scans
This is the fifth and final post in my series of posts about the deep learning model I developed to detect tumors in 3D CT scans of livers. My last post talked about visualizing the results of the computer vision project. This post will cover model accuracy and the final
Refining a deep learning model for object detection
This is the fourth post in my series about a computer vision project I worked on to identify liver tumors in CT scans. In my previous post, I had taken a break from my deep learning model to work on data management and data labeling. Now, I’ll return to the
Constructing the front of the computer vision pipeline
This is the third post describing a computer vision project I worked on at SAS to identify liver tumors in CT scans. In my previous post I talked about testing different models and hyperparameters for the models. Today, I’ll talk about data processing for image data. If you need to
Learning to use AI to detect cancer
Recently, I was given an amazing opportunity to work on a project in biomedical image analytics in collaboration with a large university medical center. The goal of the project was to develop a computer vision system that identifies tumors in CT scans of livers. I have always loved applying technology