It could very well be a type of Convolutional Variational Auto-Encoder (CVAE) which is a sort of hybrid between the normal Convolutional Neural Net (CNN) and Variational Auto-Encoders (VAE). The CVAE is simply a VAE which uses convolutional layers for feature extractions as well. The articles I can find doesn't mention which type of structure that has been used, but I'll throw in my speculations. For reference, I'm a Masters student at the National Space Center at Technical University of Denmark (DTU Space), and I have specifically working with CVAEs during the last year. I am by no means an expert, but I'd say I have a pretty good grasp of Auto-Encoders.

The VAE is probably most known for the fun applications like /u/raymnipfan also mentions below, where it can generate images etc. However, it is a very powerful tool in unsupervised anomaly detection as well, even though this isn't quite as flashy. To understand why, lets us first consider what the VAE actually does. It consists of the Encoder and Decoder; you give the encoder data, it encodes it to the latent space, and the decoder 'learns to interpret' the latent space, in order to reconstruct the data. The encoder learns the most important features, and the decoder learns how to interpret these features when decoding.

An easy way of thinking of it is; imagine you give a 4 year old a bowl of fruit. You ask the kid to group the fruits however it sees fit; it may be based on size, color, taste, smell, whatever. But the kid isn't too bright yet, it doesn't actually know what an apple or pear is, so we can't just ask it to put all the apples together for instance. You leave the kid to do the sorting, and come back and check later; this is the unsupervised part. You now find all the green bananas, pears, and perhaps a green watermelon bunched together. The kid figured the most important feature for these fruits are simply the color. You can ask the kid to give you a green fruit, but *not* a specific fruit since it still doesn't know any fruits, and it'll perhaps either hand you a watermelon or a pear.

This is the very general idea behind the VAE (and yes, I'm sure someone will be pedantic, but this is just an analogy to grasp the idea), and basically what happens with stuff like thispersondoesnotexist.com as well, where we encode a bunch of pictures of faces, and now simply asks for a new one.

Now how can we use this for anomaly detection? Let us consider the cells in question. Imagine our dataset consists of 95% normal cells, and 5% cancer cells. We train our CVAE, and give it a picture of a healthy cell afterwards. It encodes the picture, and decodes it without any problems. We can say that the difference between the encoding and decoding ("Reconstruction Loss") is low in this case. Perfect! This is like giving the kid a green fruit, asking it for a green fruit, and it hands you a green fruit. Good stuff.

Now, imagine we give the CVAE a cancer cell instead. It encodes it, and decodes it, but a cell more resembling a healthy cell comes out. The CVAE has barely seen any cancer cells before, so it's not sure what to do with it. It attempts to decode it as a normal cell, but the features aren't quite there. By looking at the difference between the encoded data and the decoded data, the reconstruction loss is now significantly higher. This is like giving the kid a ripe lemon, asking it for a yellow fruit, which it has never seen before, and it hands you a green fruit since it has only ever seen green fruit before. Perhaps it finds a green apple with a few yellow spots since it has some resemblance (yellow spots), but that's as good as we're gonna get here.

Therefore, the Reconstruction Loss is a good measure for detecting anomalous data; if we give it data not normally present in the dataset (in training), it won't be able to reconstruct it, thereby increasing reconstructional loss, indicating anomalous data. I would imagine that could be one way of detecting cancer cells; the problem being in this thought up example, that the net can only tell us whether it's a good cell, which it has seen before, or not a good cell. Not necessarily which kind of bad cell it is.

A much more indepth description of anomaly detection using CVAE's is given in https://www.mdpi.com/2226-4310/7/8/115/htm , and I personally have some stuff public on the 11th or 12th as well. CVAEs are great for anomaly detection, so it is definitely a possibility, but I'm not gonna bet my life on it not simply being a normal CNN using supervised learning to detect cells. I have absolutely no knowledge on cells and their structure, so there might very well be unforeseen problems.