Dealing with class imbalances image classification

Hi, so this question isn’t necessarily new, but I was wondering whether there meanwhile is some good approach or recommendation?

So short overview: I want to predict classes from pathology cancer slides.
The classes I want to predict have a big imbalance: So I have 350+ cases from which I want to predict 4 different classes (single label classification/but maybe I will also try label smoothing later with multiclass prediction).

The most rare class I want to predict has a little less than 20 cases, the most common class has about 140 cases.

From each case I will use about 50 extracted tiles out of the annotation, each generated tile is 512*512 pixels for now (maybe I will change the numbers per case later).

I wanted to use something like SMOTE as random oversampling isn’t as effective from what I read I think but apparently SMOTE is not directly usable with image classification? - Nonetheless it was used in an x-ray detection problem just some time ago (they predicted 1024 features of images and used those to do SMOTE afterwards): https://www.medrxiv.org/content/10.1101/2020.04.13.20063461v1

I also found this repo, which uses SMRT, which is apparently similiar to SMOTE and applicable for images? Synthetic Minority Reconstruction Technique ([SMRT])

And I mean I could also use image augmentation like blurring, elastic deformation, color transforms, etc but I was just wondering whether there would still be something better to use?

I also read something about using variational autoencoders to generate mor synthetic data, but I have no idea about variational autoencoder yet.

Maybe someone an idea or recommendation?

I would recommend to search in the forums. There are several related issues. Also, you can search in kaggle competitions to see how kagglers handles class imbalance.