Do you have lots of unlabelled data, and if so, do you do any self-supervised pre-training?
Have you ever considered releasing the backbone weights for the pre-trained models you have? No idea if this would be possible without giving up core IP, but I know I'm personally dying for an alternative to Imagenet (COCO for you?) trained on a big dataset.
Are the images in your set diverse enough that you'd expect the backbone to be a good general feature extractor?
We do have lots of unlabelled data, and we're also labeling a large portion of it. We do transfer learn for all of the models we're training, and the first backbone we use is partially self-supervised. Seems to help in overall performance, but it's not a huge effect in our experience.
Maybe once we get a lot of models we can release the backbone weights for nuclear segmentation or at least a competition set of some data we've labeled. Some IP issues here though.
What kind of alternative are you looking for? Specifically one for cells, or just for biologics in general? I'm guessing you're trying to have a better base of weights to transfer off so you can train your own model?
I would say we have medium diversity in terms of images - I think unless you have a similar application right now, you'd be better off transferring off of Imagenet just due to the amount of labeled data.
So a model that has been pretrained on something else, with enough variability to work as a feature extractor, but closer to the problem framing I mention would be of interest.
For state of the art computer vision stuff, most of the benchmarks use imagenet or similar datasets. But unfortunately I'm coming around to the realisation that those datasets are not representative of most real world problems (except general purpose scene / object recognition). So it becomes very challenging to pick out a potential technique to apply, and hope it transfers.