First, your business model isn't really clear, as what you've described so far sounds more like a research project than a go-to-market premise. Computational pathology is a crowded market, and the main players all have two things in common: access to huge numbers of labeled whole-slide images, and workflows designed to handle such images. Without the former, your project sounds like a non-starter, and given the latter, the idea you've pitched doesn't seem like an advantage. Notably, some of the existing models even have open weights (e.g. Prov-GigaPath, CTransPath).
Second, you've talked about using this approach to make diagnoses, but it's not clear exactly how this would be pitched as a market solution. The range of possible diagnoses is almost unlimited, so a useful model would need training data for everything (not possible). My understanding is that foundation models solve this problem by focusing on one or a few diagnoses in a restricted scope, e.g. prostate cancer in prostate core biopsies. The other approach is to screen for normal in clearly-defined settings, e.g. Pap smears, so that anything that isn't "normal" is flagged for manual review. Either approach, as you can see, demands a very different training and market positioning strategy.
Finally, do you have pathologists advising you, and have you done any sort of market analysis? Unless you're already a pathologist (and probably even if you were), I suspect that having both would be of immense value in deciding a go-forward plan.
All the best!