Imagine you have a photo of a cat sitting in a garden. If you want to describe the cat to someone who has never seen it, you might say it has pointy ears, a furry body, and green eyes. These details are the features that make the cat unique and distinguishable.
Similarly, in medical imaging, ML algorithms like CNN are widely used to analyze images like X-rays or MRIs. The CNN works like a set of filters that look for specific features in the image, such as edges, corners, or textures, and then combines these features to create a representation of the image.
For example, when looking at a chest X-ray, a CNN can detect features like the shape of the lungs, blood vessels, and other structures. By analyzing these features, CNN can identify patterns that indicate the presence of a disease like pneumonia or lung cancer. The CNN can also analyze other medical images, like MRIs, to detect tumors, blood clots, or other abnormalities.
To perform feature extraction, CNN applies a series of convolutional filters to the image, each designed to detect a specific pattern or feature. The filters slide over the image, computing the dot product between the filter and the corresponding pixel values in the image to produce a new feature map. These feature maps are then passed through non-linear activation functions to increase the discriminative power of the network. CNN then down-samples the feature map to increase the robustness of the network to translation and rotation. This process is repeated multiple times in a CNN, with each layer learning more complex features based on the previous layers. The final output of the network is a set of high-level features that can be used to classify or diagnose medical conditions.
Now let’s use feature extraction in a sentence!
Serious: “How can we ensure that the features extracted by a model are truly representative of the underlying data and not biased towards certain characteristics or attributes?”
My sister: “You know, finding the right filter for my selfie is like performing feature extraction on my face.”
Me: “I guess you’re just trying to extract the most Instagram-worthy features right?”