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Cool project, and helpful for learning. One concern: I don't believe the rates that you see "concerning" vs "not-concerning" in the app match the population r
by jonahx 1y ago
Cool project, and helpful for learning.
One concern:
I don't believe the rates that you see "concerning" vs "not-concerning" in the app match the population rates. That is, a random "mole-like spot or thingy" on a random person will have have a much lower base rate of being cancerous than the app would suggest.
Of course, this is necessary to make the learning efficient. But unless you pair it with base rate education it will create a bias for over-concern.
- sungam 1y agoYes you are right - the representation is biased due to the image dataset that I have used. I don't think it would be useful to match the population distribution since the fraction of skin cancers would be tiny (less than 1:1000 of the images) so users would not learn what a skin cancer looks like, however in the next version I will make it closer to 50:50 and highlight the difference from the population distribution.
- jonahx 1y agoYes. As I said matching the population base rate wouldn't be practical, so you'd need to educate on that separately from the identification learning. Let's say I achieve a 95% on the app though. Most people would have a massively over-inflated sense of their correctness in the wild. If the actual fraction is only 1/1000 and I see a friend with a lesion I identify as concerning, then my actual success rate would be: 1*0.95 / (0.05*999 + 1*0.95) So ~1.8%, not 95%. Few people understand Bayesian updating.
- sungam 1y agoThanks for this - I need to look at this more carefully