That's a bit of an optimistic take. For example, I'm familiar with a number of areas with the imaging community is getting much better at academic challenges, but the resulting models all generalize quite poorly. This has a lot to do with the lack of sufficient labeled data, but it doesn't help that the understanding of tumor morphology is changing pretty rapidly.
It's true that screening is a particularly interesting application because of the issues of fatigue and low true positive rates. On the other hand, decades ago (i.e. well before deep learning approaches) we had clinically approved classifiers that did better than average radiologists for some of these tasks and the uptake still hasn't been that impressive. Lot's of non-technical issues around making stuff like this standard of care.
I'm definitely an optimist about this, and I have an interest in it. So, grains of salt. But things worth noting:
The lack of labeled data is definitely a challenge, as you call out. But a sizable chunk of what we do is power a platform and network of pathologists to get this data within hours for training purposes. We think there will always be a very real need for human pathologists, but that the bread-and-butter work in pathology can be better handled by well-trained
and thoroughly-validated algorithms.
And yeah, the non-technical issues are just as important as the technical ones:
* There's very limited use of digital imagery in clinical pathology at all. Fortunately, that's not the case in research pathology, and the success we've had in that field has been moving clinical labs toward an investment in digital pathology.
* Reimbursement (in the US) will be an issue. There are only a few options for billing payers for pathology reads, and they aren't necessarily in lock-step with the potential future of the industry.
* Like I mentioned, this opens up a class of analysis that just isn't feasible for humans to perform. It's up to us to show the value of this type of analysis.
* The regulatory environment is a real thing. We aren't hiding from this, and are creating processes that allow us to build and iterate software like we'd like to, while still faithfully meeting our regulatory burden.
So far, we've found our approach to be viable, and we've had some really strong early results with our customers (and solid revenue!). So I'm pretty optimistic, for sure.
I believe these techniques will have a huge impact on how we do pathology as well as things like screening radiology, and that part of that will be by breaking down the silos such specialties work in, at least to a agree. I also think we have quite a way to go on the technical side but it is achievable (not to do everything people dream of, but to make significant improvements).
I also think it will take much, much longer than most people on the research side believe (hope?) to even approach standard of care. These systems are not built to move fast.
I'm glad to hear you are getting good/interesting results, and hope you are focusing more on validation and breadth of data acquisition than a lot of groups do :)
> the resulting models all generalize quite poorly
In this field, this kind of technology will be augmentative for the near-term at least, so generalization matters less than elsewhere provided the false negative rate is kept low enough. Outliers can be flagged for direct inspection.
It's a mistake to think that generalization doesn't matter much. In this case your false positive rate can go through the roof also, to the degree the system becomes useless. More generally it means you don't understand well how your system will work on real world data, so can end up with lots of unfortunate surprises.
It's true that if you are assuming a human/algorithm team some things are easier, but that doesn't make the problem go away.
It's true that screening is a particularly interesting application because of the issues of fatigue and low true positive rates. On the other hand, decades ago (i.e. well before deep learning approaches) we had clinically approved classifiers that did better than average radiologists for some of these tasks and the uptake still hasn't been that impressive. Lot's of non-technical issues around making stuff like this standard of care.