Problem is though for most of these diseases there just aren't the number of samples available, period, to do it the "right way". HCC for example, has around 50K new cases/year in the US. Even if every single case went into a repository with perfect labels, would still take a long time to collect that info. Not to mention you need either a radiologist (4 year of medical school + 6 years of post-school training) or a very skilled and experienced technician to label the data.
Not to mention imaging protocols are not standardized, and the imaging technology is also evolving so scans we do today may not be "correct" or standard in 5-10 years.
Definitely diseases have different challenges. Breast cancer screening being a notable outlier as far as data availabilty. For some diseases ML is probably always going to be problematic although may help in diagnostics mostly by helping get rid of other possibilities.
I suspect we have similar overall views of the problem, but I'm pretty strongly in camp that recent advances in ML/AI are mostly really driven by data & label availability, not algorithmic advances - this colors where I think the wins to be had in medical ML can happen most easily. Either way though the non-technical barriers seem clearly higher than the technical ones still.
Really enjoyed your contributions to this thread, thanks.
I’m a first year radiology registrar (PGY3) in Australia looking to find others doing interesting work in this domain, if you think I could help with your efforts feel free to DM
Not to mention imaging protocols are not standardized, and the imaging technology is also evolving so scans we do today may not be "correct" or standard in 5-10 years.