Classifying ultrasound videos with a small dataset I have a small dataset of ~300 ultrasound clips, about evenly divided between 3 classes. Due to the nature of the data (medical) it's difficult to get more.

I need to train a model (or an ensemble) to classify these clips. 

Feature reduction is a necessary first step. I've considered using HOGS (for example) on a cropped part of the video to create relatively small set of time-series features. 
However, even a "dumbed down" version of HOGS would give at least 100 time series features.

That being said, lets assume for a minute I'm able to reduce the feature space even more while maintaining variability between classes.

Which model(s) would be best suited for this problem?
One option in similar problems is to use transfer learning on raw images. For similar problems (few hundred images for training), solution that worked for me is transfer learning + image pre-processing to generate augmented images (E.g.: Stretched / Rotated versions of images).

High level design is :


Use a model that has been trained on large number of images (millions). This model has already learnt image processing as a domain (such as edge detection, phase / color changes and so on) and might remove need to feature engineering (like HOGS)
Add few more layers to be trained for Ultrasound images 
Train these new layers with images (Existing layers are frozen since they are already trained) 




Examples of this approach are :

https://www.researchgate.net/publication/311094437_Transfer_Learning_with_Convolutional_Neural_Networks_for_Classification_of_Abdominal_Ultrasound_Images

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5359213/

https://www.sciencedirect.com/science/article/pii/S2095809918301887