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Look! You’ve finally made it to Canada! You gloriously take in the view of Lake Ontario when your friend beside you exclaims, “Look, they have beaver tails!” You excitedly scan the lake, asking, “Where?” 

“There!”

“Where?”

“There!”

You see no movement from the lake. It isn’t until your friend pulls you to the front of a storefront says “BeaverTails” with a picture of delicious pastries that you realize they didn’t mean actual beavers’ tails. It turns out you were looking at the wrong place the whole time!

Often times, it’s easy for us to quickly identify objects because we know the context of where things should be. These are the kinds of things we take for granted, until it’s time to hand the same tasks over to machines. 

In medical imaging, experts label what are called Regions of Interests (ROIs), which are specific areas of a medical image that contain pathology, such as the specific area of a lesion. Having labelled ROIs are important, as it can help prevent extra time from being wasted on analyzing non-relevant areas of an image, especially since medical images contain complex structures that take time to interpret. But in the area of machine learning (ML) in medical imaging, having labelled ROIs is also useful because it can help with training ML models that classify whether a medical image contains a pathology or not; with ROIs identified, cropping can be done during the preprocessing of images so that only relevant areas of images are compared for the model to learn differences between positive and negative images faster.

In fact, having ROIs is so important, there is an entire field in artificial intelligence dedicated to it: Computer Vision. The field of computer vision focuses on automating the extraction of ROIs in images or videos, which plays a critical role in the mechanization of tasks like object detection and tracking for things like self-driving cars. In object detection, for example, things like ROI Pooling can be utilized; this is where multiple ROIs are used to obtain input feature maps, from which maximum values are used to detect the presence of features, giving rise to the ability to identify many objects at once – this is extremely useful, especially once you’re on the road and there are 10 other cars around you!

Now, the fun part: using Region of Interest in a sentence!

Serious: The coordinates of ROIs are given for the positive mammogram images in the dataset I’m using. Maybe I could use Grad-CAM to see if the ML breast cancer classification model I’m using uses the same regions of the image to arrive at its classification decision; this way, I can see if its decision making aligns with the decision making of radiologists.

Less serious: I forced my friend to watch my favorite movie with me, but I can’t lie – I think the attractive male lead was her only region of interest!

See you in the blogosphere,

Yan Qing Lee

According to the dictionary, the term “adversarial” refers to a situation where two parties or sides oppose each other. But what about the “adversarial example”? Does it imply an example of two opposing sides? In a way, yes.

In machine learning, an example is one instance of the dataset. Adversarial examples are examples with calculated and imperceptible perturbation that tricks the model into the wrong prediction but look the same to humans. So “adversarial”, in this case, indicates opposition between something (or human) and the model. The adversarial examples are intentionally crafted to trick the model by exploiting its vulnerabilities.

How it works? There are many ways to find weak spots and generate adversarial examples, but FGSM is one classic way, and the goal is to make small changes to a picture such that it outputs the wrong prediction. First, we input the model with the picture. Assume the model outputs the correct prediction, so the loss function, which represents the difference between the prediction and the true label, will be low. Second, we compute the gradient of the loss function to tell us whether we should add or subtract a certain value epsilon to each pixel to make the loss bigger. Epsilon is typically very small, resulting in a tiny change to the value. Now, we have a picture that looks the same as the original but will trick the model into the opposite prediction!

One exciting property of adversarial examples is their transferability. It is known that adversarial examples created for one model can also trick other unknown models. This might be due to inherent flaws in the pattern recognition mechanisms of all models and, sometimes, model similarities, allowing these adversarial examples to exploit common vulnerabilities and lead to incorrect predictions.

Now, use “adversarial example” in a sentence by the end of the day: 

Kinda Serious: “Oh I can’t believe my eyes. I am seeing a dog right here and the model says it’s a cupcake…So you’re saying it might be an adversarial image? What even is that? The model is just dumb.”

Less Serious: Apparently, the movie star has an adversarial relationship with the media, but which stars have a good relationship with the media nowadays?

See you in the blogosphere,

Yuxi Zhu

Imagine you’re trying to compare two images—not just any images, but complex medical images like MRIs or X-rays. You want to know how similar they are, but traditional methods like simply comparing pixel values don’t always capture the whole picture. This is where Learned Perceptual Image Patch Similarity, or LPIPS, comes into play.

Learned Perceptual Image Patch Similarity (LPIPS) is a cutting-edge metric for evaluating perceptual similarity between images. Unlike traditional methods like Structural Similarity Index (SSIM) or Peak Signal-to-Noise Ratio (PSNR), which rely on pixel-level analysis, LPIPS utilizes deep learning. It compares images by passing them through a pre-trained convolutional neural network (CNN) and analyzing the features extracted from various layers. This approach allows LPIPS to capture complex visual differences more closely aligned with human perception. It is especially useful in applications such as evaluating generative models, image restoration, and other tasks where perceptual accuracy is critical.

Why is this important? In medical imaging, where subtle differences can be crucial for diagnosis, LPIPS provides a more accurate assessment of image quality, especially when images have undergone various types of degradation, such as noise, blurring, or compression.

Now, let’s use LPIPS in sentences!

Serious: When evaluating the effectiveness of a new medical imaging technique, LPIPS was used to compare the generated images to the original scans, showing that it was more sensitive to perceptual differences than traditional metrics.

Less Serious: I used LPIPS to compare my childhood photos with recent ones. According to the metric, I’ve definitely “degraded” over time!

See you in the blogosphere!

Jingwen (Lisa) Zhong

Volumetric rendering stands at the forefront of visual simulation technology. It intricately models how light interacts with myriad tiny particles to produce stunningly realistic visual effects such as smoke, fog, fire, and other atmospheric phenomena. This technique diverges significantly from traditional rendering methods that predominantly utilize geometric shapes (such as polygons in 3D models). Instead, volumetric rendering approaches these phenomena as if they are composed of an immense number of particles. Each particle within this cloud-like structure has the capability to absorb, scatter, and emit light, contributing to the overall visual realism of the scene. 

This is not solely useful for generating lifelike visual effects in movies and video games; it also serves an essential function in various scientific domains. Volumetric rendering enables the visualization of intricate three-dimensional data crucial for applications such as medical imaging, where it helps in the detailed analysis of body scans, and in fluid dynamics simulations, where it assists in studying the behavior of gases and liquids in motion. This technology, thus, bridges the gap between digital imagery and realistic visual representation, enhancing both our understanding and our ability to depict complex phenomena in a more intuitive and visually engaging manner. 

How does this work? 

Let’s start by talking about direct volume rendering. Instead of trying to create a surface for every object, this technique directly translates data (like a 3D array of samples, representing our volumetric space) into images. Each point in the volume, or voxel , contains data that dictates how it should appear based on how it interacts with light. 

For example, when visualizing a CT scan, certain data points might represent bone, while others might signify soft tissue. By applying a transfer function—a kind of filter—different values are mapped to specific colors and opacities. This way, bones might be made to appear white and opaque, while softer tissues might be semi-transparent. 

The real trick lies in the sampling process. The renderer calculates how light accumulates along lines of sight through the volume, adding up the contributions of each voxel along the way. It’s a complex ballet of light and matter, with the final image emerging from the cumulative effect of thousands, if not millions, of tiny interactions. 

Let us make this a bit more concrete. We first have transfer functions, a transfer function maps raw data values to visual properties like color and opacity. Let us represent the color assigned to some voxel as C(v) and the opacity as α(v). For each pixel in the final image, a ray is cast through the data volume from the viewer’s perspective. For this we have a ray equation: 

Where P(t) is a point along the ray at parameter 𝑡, P₀ is the ray’s origin, and is the normalized direction vector of the ray. As the ray passes through the volume, the renderer calculates the accumulated color and opacity along the ray. This is often done using compositing, where the color and opacity from each sampled voxel are accumulated to form the final pixel color. 

You probably used Volumetric Rendering 

Volumetric rendering transforms CT and MRI scans into detailed 3D models, enabling doctors to examine the anatomy and functions of organs in a non-invasive manner. A specific application includes most of the modern CT viewers. Volumetric rendering is key in creating realistic simulations and environments. In most AR applications, it is used under the hood to overlay interactive, three-dimensional images on the user’s view of the real world, such as in educational tools that project anatomical models for medical students. 

Now for the fun part (see the rules here), using volume rendering  in a sentence by the end of the day: 

Serious: The breakthrough in volumetric rendering technology has enabled scientists to create highly detailed 3D models of the human brain. 

Less Serious: I tried to use volumetric rendering to visualize my Netflix binge-watching habits, but all I got was a 3D model of a couch with a never-ending stream of pizza and snacks orbiting around it. 

…I’ll see you in the blogosphere. 

You might be thinking, “KL Divergence? Sounds exotic. Is it something to do with the Malaysian capital (Kuala Lumpur) or a measurement (kiloliter)?” Nope, and nope again! It stands for Kullback-Leibler Divergence, a fancy name for a metric to compare two probability distributions.

But why not just compare their means? After all, who needs these hard-to-pronounce names? Kullback… What was it again? That’s a good point! Here’s the catch: two distributions can have the same mean but look completely
different. Imagine two Gaussian distributions, both centered at zero, but one is wide and flat, while the other is narrow and tall. Clearly, not similar!

So, maybe comparing the mean and variance would work? Excellent thinking! But what if the distributions aren’t both Gaussian? For example, a wide and flat Gaussian and a uniform distribution (totally flat) might look similar visually, but the uniform distribution is not parametrized by a mean or variance. So, what do we compare?

Enter KL Divergence!

KL Divergence returns a single number that tells us how similar two distributions are, regardless of their types. The smaller the number, the more similar the distributions. But how do we calculate it? Here’s the formula (don’t worry, you don’t have to memorize it!).

Notice, if the distribution q has probability mass where p doesn’t, the KL Divergence will be large. Good, that’s what we want! But, if q has little mass where p has a lot, the KL Divergence will be small. Wait, that’s not what we want! No, it’s not, but luckily KL Divergence is asymmetric! KL(q || p) returns a different value than KL(p || q), so
we can compute both! Why are they different? I’ll leave that up to you to figure out!

KL Divergence in Action

Now, the fun part: using KL Divergence in a sentence!

Serious: Professor, can we approximate one distribution with another by minimizing the KL Divergence between them? That’s a great question! You’ve just stumbled on the idea behind Variational Inference.

Less Serious: Ladies and gentlemen, the KL Divergence between London and Kuala Lumpur is large, and so our flight time today will be 7 hour and 30 minutes. Please remember to stow your hand luggage in the overhead bins above you, fold your tray tables, and fasten your seatbelts.

See you in the blogosphere,
Benedek Balla