- Unsupervised Learning – The computer relies on patterns in all images to identify variables, without "knowing" what the image is. This is best for anomaly detection and differentiating stand-alone activities from continuous ones.
- Reinforcement learning – The computer learns from its mistakes again and again until it completes the task error free and without being directed. This is often used in robotics and navigation.
- Semi-supervised – Groups of different variables are assigned in different ways, with the algorithm over time learning the most optimal approach. Used in speech recognition, web page classification, and image recognition and classification.
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A more accurate and faster form of machine learning, deep learning does not require as much upfront, as the tools and framework are already built in.. It can train networks and adjust variables within them. The downside is the greater amounts of data required for training, compared to standard machine learning.
- Convolutional – Extracts complex features of data at each level to determine the output. This is suited well for noisier images that contain features other than the specific information one is looking for.
- Recurrent Networks – Form of deep learning that stores information in context nodes so that the machine can learn data sequences and output another sequence. This is especially helpful for real language translation.
- Generative Adversarial Networks (GANS) – Composed of two neural networks — one of which produces real images, another which produces false images. These networks teach one another to detect fake from real data and generate information that is indistinguishable. With it, users could potentially create a healthy 2D or 3D image of a scan for a person with a tumor to deceive radiologists and oncologists. “All kinds of bad applications can happen from this. We certainly have to find a way around this because it is just too easy to do at this point,” said Garel.
He adds that if used correctly, all of these and other tools available can help speed up data cleaning and ensure faster completion of work orders for workflow and quality patient care.
“Our hypothesis is that we can apply data science to clean up enough of this data so we can actually make it useful to generate results. That’s the goal.”Back to HCB News