Home Artificial Intelligence Entropy based Uncertainty Prediction The importance of Uncertainty in Image Segmentation Understanding Entropy Implementing Entropy in Image Segmentation Case Study: Medical Imaging Make Informed Decisions Important Takeaways References

Entropy based Uncertainty Prediction The importance of Uncertainty in Image Segmentation Understanding Entropy Implementing Entropy in Image Segmentation Case Study: Medical Imaging Make Informed Decisions Important Takeaways References

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Entropy based Uncertainty Prediction
The importance of Uncertainty in Image Segmentation
Understanding Entropy
Implementing Entropy in Image Segmentation
Case Study: Medical Imaging
Make Informed Decisions
Important Takeaways
References

This text explores how Entropy could be employed as a tool for uncertainty estimation in image segmentation tasks. We are going to walk through what Entropy is, and easy methods to implement it with Python.

Towards Data Science
Photo by Michael Dziedzic on Unsplash

While working at Cambridge University as a Research Scientist in Neuroimaging and AI, I faced the challenge of performing image segmentation on intricate brain datasets using the most recent Deep Learning techniques, especially the nnU-Net. During this endeavor, I observed a major gap: the overlooking of uncertainty estimation. Yet, uncertainty is crucial for reliable decision-making.

Before delving into the specifics, be at liberty to envision out my Github repository which incorporates all of the code snippets discussed in this text.

On the earth of computer vision and machine learning, image segmentation is a central problem. Whether it’s in medical imaging, self-driving cars, or robotics, accurate segmentation are vital for effective decision-making. Nonetheless, one often missed aspect is the measure of uncertainty related to these segmentations.

Why should we care about uncertainty in image segmentation?

In lots of real-world applications, an incorrect segmentation could end in dire consequences. For instance, if a self-driving automotive misidentifies an object or a medical imaging system incorrectly labels a tumor, the results might be catastrophic. Uncertainty estimation gives us a measure of how ‘sure’ the model is about its prediction, allowing for better-informed decisions.

We can even use Entropy as a measure of uncertainty to enhance the educational of our neural networks. This area is knows as ‘lively learning’. This concept will likely be explored in further articles however the most important idea is to discover the zones on which the models are essentially the most uncertain to concentrate on them. For instance we could have a CNN performing medical image segmentation on the brain, but performing very poorly on subjects with tumours. Then we could concentrate our efforts to accumulate more labels of this kind.

Entropy is an idea borrowed from thermodynamics and data theory, which quantifies the quantity of uncertainty or randomness in a system. Within the context of machine learning, entropy could be used to measure the uncertainty of model predictions.

Mathematically, for a discrete random variable X with probability mass function P(x), the entropy H(X) is defined as:

Or within the continous case:

The upper the entropy, the greater the uncertainty, and vice versa.

A classic example to completely grasp the concept:

Situation 1: A biased coin

Photo by Jizhidexiaohailang on Unsplash

Imagine a biased coin, which lands on head with a probability p=0.9, and tail with a probability 1-p=0.1.

Its entropy is

Situation 2: Balanced coin

Now let’s imagine a balanced coin which lands on head and tail with probability p=0.5

Its entropy is:

The entropy is larger, which is coherent with what we said before: more uncertainty = more entropy.

Actually it’s interesting to notice that p=0.5 corresponds to the utmost entropy:

Entropy visualisation, Image by creator

Intuitively, keep in mind that a uniform distribution is the case with maximal entropy. If every consequence is equally probable, then this corresponds to the maximal uncertainty.

To link this to image segmentation, consider that in deep learning, the ultimate softmax layer normally provides the category probabilities for every pixel. One can easily compute the entropy for every pixel based on these softmax outputs.

But How does it work?

When a model is confident about a specific pixel belonging to a particular class, the softmax layer shows high probability (~1) for that class, and really small probabilities (~0) for the opposite classes.

Softmax layer, confident case, Image by creator

Conversely, when the model is uncertain, the softmax output is more evenly spread across multiple classes.

Softmax layer, uncertain case, Image by creator

The possibilities are way more diffuse, near the uniform case when you remember, since the model cannot resolve which class is related to the pixel.

If you’ve made it until now, great! You must have an ideal intuition of how entropy works.

Let’s illustrate this with a hands-on example using medical imaging, specifically T1 Brain scans of fetuses. All codes and pictures for this case study can be found in my Github repository.

1. Computing Entropy with Python

As we said before, we’re working with the softmax output tensor, given by our Neural Network. This approach is model-free, it only uses the possibilities of every class.

Let’s make clear something vital concerning the dimensions of the tensors we’re working with.

When you are working with 2D Images, the form of your softmax layer must be:

Meaning that for every pixel (or voxel), we have now a vector of size Classes, which provides us the possibilities of a pixel to belong to every of the classes we have now.

Subsequently the entropy must be computer along the primary dimension:


def compute_entropy_4D(tensor):
"""
Compute the entropy on a 4D tensor with shape (number_of_classes, 256, 256, 256).

Parameters:
tensor (np.ndarray): 4D tensor of shape (number_of_classes, 256, 256, 256)

Returns:
np.ndarray: 3D tensor of shape (256, 256, 256) with entropy values for every pixel.
"""

# First, normalize the tensor along the category axis in order that it represents probabilities
sum_tensor = np.sum(tensor, axis=0, keepdims=True)
tensor_normalized = tensor / sum_tensor

# Calculate entropy
entropy_elements = -tensor_normalized * np.log2(tensor_normalized + 1e-12) # Added a small value to avoid log(0)
entropy = np.sum(entropy_elements, axis=0)

entropy = np.transpose(entropy, (2,1,0))

total_entropy = np.sum(entropy)

return entropy, total_entropy

2. Visualizing Entropy-based Uncertainty

Now let’s visualize the uncertainties through the use of a heatmap, on each slice of our image segmentation.

T1 scan (left), Segmentation (middle), Entropy (Right), Image by creator

Let’s have a look at an other example:

T1 scan (left), Segmentation (middle), Entropy (Right), Image by creator

The outcomes look great! Indeed we will see that that is coherent since the zones of high entropy are on the contour of the shapes. That is normal since the model does not likely doubt the points at the center of every zone, but its somewhat the delimitation or contour that’s difficult to identify.

This uncertainty could be utilized in loads of alternative ways:

  1. As health workers work an increasing number of with AI as a tool, being aware of the uncertainty of the model is crucial. This mean that health workers could spend more times on the zone where more fine-grained attention is required.

2. Within the context of Energetic Learning or Semi-Supervised Learning, we will leverage Entropy based Uncertainty to concentrate on the examples with maximal uncertainty, and improve the efficiency of learning (more about this in coming articles).

  • Entropy is a particularly powerful concept to measure the randomness or uncertainty of a system.
  • It is feasible to leverage Entropy in Image Segmentation. This approach is model free and only uses the softmax output tensor.
  • Uncertainty estimation is missed, however it is crucial. Good Data Scientists know easy methods to make good models. Great Data Scientists know where their model fail and use this to enhance learning.

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